import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.graph_objects as go
import plotly.express as px
from plotly.subplots import make_subplots
import dataframe_image as dfi
import adjustText as aT
from jupyterthemes import jtplot
#import requests
import cufflinks as cf
#from bs4 import BeautifulSoup as bs
jtplot.style(theme='monokai', context='notebook', ticks=True, grid=False)
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
init_notebook_mode(connected=True)
cf.go_offline()
from IPython.display import display_html
def display_side_by_side(*args):
html_str=''
for df in args:
html_str+=df.to_html()
display_html(html_str.replace('table','table style="display:inline"'),raw=True)
#Covid19 Malaysia States Dataset from MOH, Malaysia Github accounts
df_states_cases = pd.read_csv('https://raw.githubusercontent.com/MoH-Malaysia/covid19-public/main/epidemic/cases_state.csv')
df_states_deaths = pd.read_csv('https://raw.githubusercontent.com/MoH-Malaysia/covid19-public/main/epidemic/deaths_state.csv')
df_states_hosp = pd.read_csv('https://raw.githubusercontent.com/MoH-Malaysia/covid19-public/main/epidemic/hospital.csv')
df_states_icu = pd.read_csv('https://raw.githubusercontent.com/MoH-Malaysia/covid19-public/main/epidemic/icu.csv')
df_states_pkrc = pd.read_csv('https://raw.githubusercontent.com/MoH-Malaysia/covid19-public/main/epidemic/pkrc.csv')
df_states_test = pd.read_csv('https://raw.githubusercontent.com/MoH-Malaysia/covid19-public/main/epidemic/tests_state.csv')
df_states_vaksin = pd.read_csv('https://raw.githubusercontent.com/CITF-Malaysia/citf-public/main/vaccination/vax_state.csv')
df_states_pop = pd.read_csv('https://raw.githubusercontent.com/MoH-Malaysia/covid19-public/main/static/population.csv')
df_hosp_daily = df_states_hosp.groupby('date').sum().reset_index()
df_hosp_daily['admitted_covid_cum'] = df_hosp_daily['admitted_covid'].cumsum()
df_hosp_daily['discharged_covid_cum'] = df_hosp_daily['discharged_covid'].cumsum()
df_icu_daily = df_states_icu.groupby('date').sum().reset_index()
df_icu_daily['icu_covid_cum'] = df_icu_daily['icu_covid'].cumsum()
df_pkrc_daily = df_states_pkrc.groupby('date').sum().reset_index()
df_pkrc_daily['admitted_covid_cum'] = df_pkrc_daily['admitted_covid'].cumsum()
df_pkrc_daily['discharged_covid_cum'] = df_pkrc_daily['discharged_covid'].cumsum()
#Selecting the required columns
df_states_cases = df_states_cases[['date','state','cases_new','cases_recovered','cases_pvax','cases_fvax','cases_boost']]
df_date_start = df_states_cases.head(1)
df_date_end = df_states_cases.tail(1)
df_states_deaths = df_states_deaths[['date','state','deaths_new','deaths_bid_dod','deaths_pvax','deaths_fvax','deaths_boost']]
df_states_hosp = df_states_hosp[['date','state','admitted_covid','discharged_covid']]
df_states_vaksin = df_states_vaksin[['date','state','daily_partial','daily_full','daily_booster','daily']]
df_states_pop = df_states_pop[['state','pop']]
#Grouping and summation the detasets based on states columns
df_cases = df_states_cases.groupby('state').sum()
df_deaths = df_states_deaths.groupby('state').sum()
df_hosp = df_states_hosp.groupby('state').sum()
df_vaksin = df_states_vaksin.groupby('state').sum()
#Dataset for overall Malaysia Covid19
df_mas_cases = pd.read_csv('https://raw.githubusercontent.com/MoH-Malaysia/covid19-public/main/epidemic/cases_malaysia.csv')
df_mas_deaths = pd.read_csv('https://raw.githubusercontent.com/MoH-Malaysia/covid19-public/main/epidemic/deaths_malaysia.csv')
df_mas_test = pd.read_csv('https://raw.githubusercontent.com/MoH-Malaysia/covid19-public/main/epidemic/tests_malaysia.csv')
df_mas_vaksin = pd.read_csv('https://raw.githubusercontent.com/CITF-Malaysia/citf-public/main/vaccination/vax_malaysia.csv')
df_mas_pop = pd.read_csv('https://raw.githubusercontent.com/MoH-Malaysia/covid19-public/main/static/population.csv')
df_mas_aefi = pd.read_csv('https://raw.githubusercontent.com/MoH-Malaysia/covid19-public/main/vaccination/aefi.csv')
df_mas_vaksin['Total Pfizer'] = df_mas_vaksin['pfizer1'] + df_mas_vaksin['pfizer2']
df_mas_vaksin['Total Sinovac'] = df_mas_vaksin['sinovac1'] + df_mas_vaksin['sinovac2']
df_mas_vaksin['Total AstraZ'] = df_mas_vaksin['astra1'] + df_mas_vaksin['astra2']
df_mas_vaksin['Cum. Daily'] = df_mas_vaksin['daily_full'].cumsum()
df_mas_vaksin['Population'] = df_states_pop.at[df_states_pop.index[0],'pop']
df_mas_vaksin['Vax. (%)'] = (df_mas_vaksin['Cum. Daily']/df_mas_vaksin['Population'])*100
df_mas_cases = df_mas_cases[['date','cases_new','cases_recovered','cases_pvax','cases_fvax','cases_active','cases_boost']]
df_mas_deaths = df_mas_deaths[['date','deaths_new','deaths_bid_dod','deaths_pvax','deaths_fvax','deaths_boost']]
df_mas_vaksin = df_mas_vaksin[['date', 'daily_partial', 'daily_full', 'daily_booster', 'daily', 'Total Pfizer',
'Total Sinovac', 'Total AstraZ', 'cansino', 'Cum. Daily', 'Vax. (%)']]
df_mas = pd.merge(df_mas_cases, df_mas_deaths, on='date')
df_mas = pd.merge(df_mas, df_mas_vaksin, on='date')
#Merging the datasets into one datasets
df_states = pd.merge(df_cases,df_deaths,on='state')
df_states = pd.merge(df_states,df_hosp,on='state')
df_states = pd.merge(df_states,df_vaksin,on='state')
df_states = pd.merge(df_states,df_states_pop,on='state')
#Creating main tables for presentation
df_states_table = df_states
df_states_table['deaths_vax_total'] = df_states_table['deaths_pvax'] + df_states_table['deaths_fvax'] + df_states_table['deaths_boost']
df_states_table['cases_vax_total'] = df_states_table['cases_pvax'] + df_states_table['cases_fvax']+ df_states_table['cases_boost']
df_states_table = df_states_table[['state','cases_new','cases_recovered','cases_vax_total','deaths_new',
'deaths_bid_dod','deaths_vax_total','daily_partial','daily_full','daily',
'daily_booster','pop']]
df_states_table.loc['Total'] = df_states_table.sum(numeric_only=True, axis=0)
df_states_table['state'] = df_states_table['state'].replace(np.nan, 'Malaysia')
df_states_table['Cases (%)'] = df_states_table['cases_new']/df_states_table['pop']*100
df_states_table['Cases Recovered (%)'] = df_states_table['cases_recovered']/df_states_table['cases_new']*100
df_states_table['Cases Vax. (%)'] = df_states_table['cases_vax_total']/df_states_table['cases_new']*100
df_states_table['Deaths (%)'] = df_states_table['deaths_new']/df_states_table['cases_new']*100
df_states_table['Deaths Vax. (%)'] = df_states_table['deaths_vax_total']/df_states_table['deaths_new']*100
df_states_table['Deaths BID (%)'] = df_states_table['deaths_bid_dod']/df_states_table['deaths_new']*100
df_states_table['Vax. 3D (%)'] = df_states_table['daily_booster']/df_states_table['pop']*100
df_states_table['Vax. 2D (%)'] = df_states_table['daily_full']/df_states_table['pop']*100
df_states_table['Vax. 1D (%)'] = df_states_table['daily_partial']/df_states_table['pop']*100
df_states_table.rename(columns={'state':'States','cases_new':'Cases Positive','cases_recovered':'Cases Recovered',
'cases_vax_total':'Cases Vax.','deaths_new':'Death Cases',
'deaths_bid_dod':'Death BID','deaths_vax_total':'Deaths Vax.',
},inplace=True)#'daily':'Total Vaccinated','pop':'Population'
print('Report Start Date :', df_date_start.at[df_date_start.index[0],'date'])
print('Report End Date :', df_date_end.at[df_date_end.index[0],'date'])
Report Start Date : 2020-01-25 Report End Date : 2022-04-20
df_mas_cases['total_vax'] = df_mas_cases['cases_pvax'] + df_mas_cases['cases_fvax'] + df_mas_cases['cases_boost']
df_mas_cases['cum_new'] = df_mas_cases['cases_new'].cumsum()
df_mas_cases['cum_recovered'] = df_mas_cases['cases_recovered'].cumsum()
df_mas_cases['cum_vax'] = df_mas_cases['total_vax'].cumsum()
df_mas_cases['active_cum'] = df_mas_cases['cases_active'].cumsum()
df_mas_deaths['total_vax'] = df_mas_deaths['deaths_pvax'] + df_mas_deaths['deaths_fvax'] + df_mas_deaths['deaths_boost']
df_mas_deaths['cum_deaths'] = df_mas_deaths['deaths_new'].cumsum()
df_mas_deaths['cum_vax'] = df_mas_deaths['total_vax'].cumsum()
df_mas_monthly = df_mas_cases.append(df_mas_deaths)
def getYearMonth(s):
return s.split("-")[1]+"-"+s.split("-")[0]
df_mas_monthly['YearMonth']= df_mas_monthly['date'].apply(lambda x: getYearMonth(x))
df_mas_monthly = df_mas_monthly.groupby('YearMonth').sum()
df_mas_monthly = df_mas_monthly.reset_index()
df_mas_monthly['vax_total_cases'] = df_mas_monthly['cases_fvax']+df_mas_monthly['cases_pvax']
df_mas_monthly['vax_total_deaths'] = df_mas_monthly['deaths_fvax']+df_mas_monthly['deaths_pvax']+df_mas_monthly['deaths_boost']
df_mas_yearly = df_mas_cases.append(df_mas_deaths)
def getYear(s):
return s.split("-")[0]
def getMonth(s):
return s.split("-")[1]
df_mas_yearly['Year']= df_mas_yearly['date'].apply(lambda x: getYear(x))
df_mas_yearly['Month']= df_mas_yearly['date'].apply(lambda x: getMonth(x))
df_mas_yearly = df_mas_yearly.groupby('Year').sum().reset_index()
df_mas_yearly['vax_total_cases'] = df_mas_yearly['cases_fvax'] + df_mas_yearly['cases_pvax'] + df_mas_yearly['cases_boost']
df_mas_yearly['vax_total_deaths'] = df_mas_yearly['deaths_fvax'] + df_mas_yearly['deaths_pvax'] + df_mas_yearly['deaths_boost']
fig = make_subplots(rows=1, cols=2, shared_xaxes=True, vertical_spacing=0.08,
subplot_titles=('<b>Yearly Total Covid19 Cases & Recovered</b>',
'<b>Monthly Total Covid19 Cases & Recovered</b>'))
#Graph 1
fig.append_trace(go.Bar(x = df_mas_yearly['Year'], y = df_mas_yearly['cases_new'],
name='Cases Positive',marker_color='red'),row=1, col=1)
fig.append_trace(go.Bar(x = df_mas_yearly['Year'], y = df_mas_yearly['cases_recovered'],
name='Cases Recovered',marker_color='blue'),row=1, col=1)
#Graph 2
fig.append_trace(go.Bar(x = df_mas_monthly['YearMonth'], y = df_mas_monthly['cases_new'],
name='Cases Positive',marker_color='red'),row=1, col=2)
fig.append_trace(go.Bar(x = df_mas_monthly['YearMonth'], y = df_mas_monthly['cases_recovered'],
name='Cases Recovered',marker_color='blue'),row=1, col=2)
#Update Fonts & Size
fig.update_annotations(font=dict(family="Helvetica", size=11))
fig.update_layout(font=dict(family="Helvetica", size=11))
fig.update_layout(height=400,showlegend=False,
title_text='Malaysia Covid19 Cases', title_x=0.5)
fig.update_xaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
fig.update_yaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
#Plotting the graph
fig.show()
fig = make_subplots(rows=1, cols=2, shared_xaxes=True, vertical_spacing=0.08,
subplot_titles=('<b>Yearly Total Covid19 Death Cases</b>',
'<b>Monthly Total Covid19 Death Cases</b>'))
#Graph 1
fig.append_trace(go.Bar(x = df_mas_yearly['Year'], y = df_mas_yearly['deaths_new'],
name='Death Cases',marker_color='red'),row=1, col=1)
fig.append_trace(go.Bar(x = df_mas_yearly['Year'], y = df_mas_yearly['vax_total_deaths'],
name='Death Vax Cases',marker_color='blue'),row=1, col=1)
#Graph 2
fig.append_trace(go.Bar(x = df_mas_monthly['YearMonth'], y = df_mas_monthly['deaths_new'],
name='Death Cases',marker_color='red'),row=1, col=2)
fig.append_trace(go.Bar(x = df_mas_monthly['YearMonth'], y = df_mas_monthly['vax_total_deaths'],
name='Death Vax Cases',marker_color='blue'),row=1, col=2)
#Update Fonts & Size
fig.update_annotations(font=dict(family="Helvetica", size=11))
fig.update_layout(font=dict(family="Helvetica", size=11))
fig.update_layout(height=400,showlegend=False,
title_text='Malaysia Covid19 Deaths Cases', title_x=0.5)
fig.update_xaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
fig.update_yaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
#Plotting the graph
fig.show()
df_2020 = df_mas_cases.query("date >= '2020-01-01' \
and date <= '2020-12-31'")
df_2021 = df_mas_cases.query("date >= '2021-01-01' \
and date <= '2021-12-31'")
df_2022 = df_mas_cases.query("date >= '2022-01-01' \
and date <= '2022-12-31'")
df_2020['cum_2020'] = df_2020['cases_new'].cumsum()
df_2021['cum_2021'] = df_2021['cases_new'].cumsum()
df_2022['cum_2022'] = df_2022['cases_new'].cumsum()
death_2020 = df_mas_deaths.query("date >= '2020-01-01' \
and date <= '2020-12-31'")
death_2021 = df_mas_deaths.query("date >= '2021-01-01' \
and date <= '2021-12-31'")
death_2022 = df_mas_deaths.query("date >= '2022-01-01' \
and date <= '2022-12-31'")
death_2020['cum_2020'] = death_2020['deaths_new'].cumsum()
death_2021['cum_2021'] = death_2021['deaths_new'].cumsum()
death_2022['cum_2022'] = death_2022['deaths_new'].cumsum()
fig = make_subplots(rows=1, cols=3, specs=[[{'secondary_y': True}, {'secondary_y': True},
{'secondary_y': True}]],
subplot_titles=('<b>Year 2020</b>',
'<b>Year 2021</b>',
'<b>Year 2022</b>'))
fig.add_trace(go.Scatter(x = df_2020['date'], y = df_2020['cases_new'], name = 'Daily Cases',
line = dict(color='blue', width=1)), secondary_y=False, row=1, col=1)
fig.add_trace(go.Scatter(x = df_2020['date'], y = df_2020['cum_2020'], name = 'Cumulative Cases',
line = dict(color='red', width=1.5)), secondary_y=True, row=1, col=1)
fig.add_trace(go.Scatter(x = df_2020['date'], y = df_2020['cases_active'], name = 'Active Cases',
line = dict(color='green', width=1.5)), secondary_y=True, row=1, col=1)
fig.add_trace(go.Scatter(x = df_2021['date'], y = df_2021['cases_new'], name = 'Cases Daily',
line = dict(color='blue', width=1)), secondary_y=False, row=1, col=2)
fig.add_trace(go.Scatter(x = df_2021['date'], y = df_2021['cum_2021'], name = 'Cases',
line = dict(color='red', width=1.5)), secondary_y=True, row=1, col=2)
fig.add_trace(go.Scatter(x = df_2021['date'], y = df_2021['cases_active'], name = 'Active Cases',
line = dict(color='green', width=1.5)), secondary_y=True, row=1, col=2)
fig.add_trace(go.Scatter(x = df_2022['date'], y = df_2022['cases_new'], name = 'Daily Cases',
line = dict(color='blue', width=1)), secondary_y=False, row=1, col=3)
fig.add_trace(go.Scatter(x = df_2022['date'], y = df_2022['cum_2022'], name = 'Cumulative Cases',
line = dict(color='red', width=1.5)), secondary_y=True, row=1, col=3)
fig.add_trace(go.Scatter(x = df_2022['date'], y = df_2022['cases_active'], name = 'Active Cases',
line = dict(color='green', width=1.5)), secondary_y=True, row=1, col=3)
fig.update_layout(title_text='Malaysia Covid19 Cases By Year', title_x=0.5, showlegend=False,
height=350)
fig.update_xaxes(title_text='')
#Update Fonts & Size
fig.update_annotations(font=dict(family="Helvetica", size=11))
fig.update_layout(font=dict(family="Helvetica", size=11))
fig.update_layout(height=350,showlegend=False)
fig.update_xaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
fig.update_yaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
#Plotting the graph
fig.show()
fig = make_subplots(rows=1, cols=3, specs=[[{'secondary_y': True}, {'secondary_y': True},
{'secondary_y': True}]],
subplot_titles=('<b>Year 2020</b>',
'<b>Year 2021</b>',
'<b>Year 2022</b>'))
fig.add_trace(go.Scatter(x = death_2020['date'], y = death_2020['deaths_new'], name = 'Daily Death Cases',
line = dict(color='red', width=1)), secondary_y=False, row=1, col=1)
fig.add_trace(go.Scatter(x = death_2020['date'], y = death_2020['cum_2020'], name = 'Cumulative Death Cases',
line = dict(color='blue', width=1.5)), secondary_y=True, row=1, col=1)
fig.add_trace(go.Scatter(x = death_2021['date'], y = death_2021['deaths_new'], name = 'Daily Death Cases',
line = dict(color='red', width=1)), secondary_y=False, row=1, col=2)
fig.add_trace(go.Scatter(x = death_2021['date'], y = death_2021['cum_2021'], name = 'Cumulative Death Cases',
line = dict(color='blue', width=1.5)), secondary_y=True, row=1, col=2)
fig.add_trace(go.Scatter(x = death_2022['date'], y = death_2022['deaths_new'], name = 'Daily Death Cases',
line = dict(color='red', width=1)), secondary_y=False, row=1, col=3)
fig.add_trace(go.Scatter(x = death_2022['date'], y = death_2022['cum_2022'], name = 'Cumulative Death Cases',
line = dict(color='blue', width=1.5)), secondary_y=True, row=1, col=3)
fig.update_layout(title_text='Malaysia Covid19 Death Cases By Year', title_x=0.5, showlegend=False,
height=350)
fig.update_xaxes(title_text='')
#Update Fonts & Size
fig.update_annotations(font=dict(family="Helvetica", size=11))
fig.update_layout(font=dict(family="Helvetica", size=11))
fig.update_layout(height=350,showlegend=False)
fig.update_xaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
fig.update_yaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
#Plotting the graph
fig.show()
df_mas_yearly_table = df_mas_yearly[['Year','cases_new','vax_total_cases','cases_recovered',
'deaths_new','vax_total_deaths']]
df_mas_yearly_table.rename(columns={'cases_new':'Cases Positive',
'cases_recovered':'Cases Recovered',
'vax_total_cases':'Cases Vax.',
'deaths_new':'Death Cases',
'vax_total_deaths':'Deaths Vax.'},inplace=True)
df_mas_yearly_table.loc['Total'] = df_mas_yearly_table.sum(numeric_only=True, axis=0)
df_mas_yearly_table['Overall Cases (%)'] = (df_mas_yearly_table['Cases Positive']/df_mas_yearly_table['Cases Positive'].sum())*200
df_mas_yearly_table['Cases Vax. (%)'] = (df_mas_yearly_table['Cases Vax.']/df_mas_yearly_table['Cases Positive'])*100
df_mas_yearly_table['Recovered (%)'] = (df_mas_yearly_table['Cases Recovered']/df_mas_yearly_table['Cases Positive'])*100
df_mas_yearly_table['Death (%)'] = (df_mas_yearly_table['Death Cases']/df_mas_yearly_table['Cases Positive'])*100
df_mas_yearly_table['Deaths Vax. (%)'] = (df_mas_yearly_table['Deaths Vax.']/df_mas_yearly_table['Death Cases'])*100
df_mas_yearly_table['Year'] = df_mas_yearly_table['Year'].replace(np.nan, 'Total')
df_mas_yearly_table = df_mas_yearly_table[['Year','Cases Positive','Overall Cases (%)',
'Cases Vax.','Cases Vax. (%)',
'Cases Recovered','Recovered (%)',
'Death Cases','Death (%)',
'Deaths Vax.','Deaths Vax. (%)']]
df_mas_yearly_table.style.set_caption("Malaysia Covid19 Cases Yearly").set_table_styles([{
'selector': 'caption',
'props': [
('color', 'black'),
('font-size', '20px'),
("text-align", "center"),
('text-decoration', 'underline'),
('font-family','Arial'),
('text-shadow', '2px 2px 5px grey')
]},(dict
(selector='th',props=[('text-align',
'left')]))]).format(
{'Cases Positive':'{:,.0f}','Cases Recovered':'{:,.0f}','Cases Vax.':'{:,.0f}',
'Death Cases':'{:,.0f}','Deaths Vax.':'{:,.0f}','Recovered (%)':'{:,.1f}',
'Death (%)':'{:,.1f}','Cases Vax. (%)':'{:,.1f}','Deaths Vax. (%)':'{:,.1f}',
'Overall Cases (%)':'{:,.1f}'
}).apply(lambda x: ['background: salmon' if x.name in ['Total'] else '' for i in x], axis=1).hide_index()
| Year | Cases Positive | Overall Cases (%) | Cases Vax. | Cases Vax. (%) | Cases Recovered | Recovered (%) | Death Cases | Death (%) | Deaths Vax. | Deaths Vax. (%) |
|---|---|---|---|---|---|---|---|---|---|---|
| 2020 | 113,010 | 2.6 | 0 | 0.0 | 88,940 | 78.7 | 471 | 0.4 | 0 | 0.0 |
| 2021 | 2,645,076 | 60.0 | 1,067,163 | 40.3 | 2,596,438 | 98.2 | 31,016 | 1.2 | 10,825 | 34.9 |
| 2022 | 1,651,116 | 37.4 | 1,385,454 | 83.9 | 1,597,635 | 96.8 | 3,978 | 0.2 | 2,608 | 65.6 |
| Total | 4,409,202 | 100.0 | 2,452,617 | 55.6 | 4,283,013 | 97.1 | 35,465 | 0.8 | 13,433 | 37.9 |
df_q_cases = df_mas_cases
df_q_deaths = df_mas_deaths
df_q_cases = df_q_cases[['date','cases_new','total_vax','cases_recovered']]
df_q_cases.rename(columns={'cases_new':'Cases Positive','cases_recovered':'Cases Recovered',
'total_vax':'Cases Vax.'},inplace=True)
df_q_deaths = df_q_deaths[['date','deaths_new','total_vax']]
df_q_deaths.rename(columns={'deaths_new':'Death Cases',
'total_vax':'Deaths Vax.'},inplace=True)
q1_cases_2020 = df_q_cases.query("date >= '2020-01-01' \
and date <= '2020-03-31'")
q2_cases_2020 = df_q_cases.query("date >= '2020-04-01' \
and date <= '2020-06-30'")
q3_cases_2020 = df_q_cases.query("date >= '2020-07-01' \
and date <= '2020-09-30'")
q4_cases_2020 = df_q_cases.query("date >= '2020-10-01' \
and date <= '2020-12-31'")
q1_deaths_2020 = df_q_deaths.query("date >= '2020-01-01' \
and date <= '2020-03-31'")
q2_deaths_2020 = df_q_deaths.query("date >= '2020-04-01' \
and date <= '2020-06-30'")
q3_deaths_2020 = df_q_deaths.query("date >= '2020-07-01' \
and date <= '2020-09-30'")
q4_deaths_2020 = df_q_deaths.query("date >= '2020-10-01' \
and date <= '2020-12-31'")
def getYear(s):
return s.split("-")[0]
q1_cases_2020['Year']= q1_cases_2020['date'].apply(lambda x: getYear(x))
q1_cases_2020 = q1_cases_2020.groupby('Year').sum().reset_index()
q1_cases_2020['Year'] = q1_cases_2020['Year'].replace('2020', 'Q1')
q2_cases_2020['Year']= q2_cases_2020['date'].apply(lambda x: getYear(x))
q2_cases_2020 = q2_cases_2020.groupby('Year').sum().reset_index()
q2_cases_2020['Year'] = q2_cases_2020['Year'].replace('2020', 'Q2')
q3_cases_2020['Year']= q3_cases_2020['date'].apply(lambda x: getYear(x))
q3_cases_2020 = q3_cases_2020.groupby('Year').sum().reset_index()
q3_cases_2020['Year'] = q3_cases_2020['Year'].replace('2020', 'Q3')
q4_cases_2020['Year']= q4_cases_2020['date'].apply(lambda x: getYear(x))
q4_cases_2020 = q4_cases_2020.groupby('Year').sum().reset_index()
q4_cases_2020['Year'] = q4_cases_2020['Year'].replace('2020', 'Q4')
q_cases_2020 = q1_cases_2020.append([q2_cases_2020,q3_cases_2020,q4_cases_2020])
q1_deaths_2020['Year']= q1_deaths_2020['date'].apply(lambda x: getYear(x))
q1_deaths_2020 = q1_deaths_2020.groupby('Year').sum().reset_index()
q1_deaths_2020['Year'] = q1_deaths_2020['Year'].replace('2020', 'Q1')
q2_deaths_2020['Year']= q2_deaths_2020['date'].apply(lambda x: getYear(x))
q2_deaths_2020 = q2_deaths_2020.groupby('Year').sum().reset_index()
q2_deaths_2020['Year'] = q2_deaths_2020['Year'].replace('2020', 'Q2')
q3_deaths_2020['Year']= q3_deaths_2020['date'].apply(lambda x: getYear(x))
q3_deaths_2020 = q3_deaths_2020.groupby('Year').sum().reset_index()
q3_deaths_2020['Year'] = q3_deaths_2020['Year'].replace('2020', 'Q3')
q4_deaths_2020['Year']= q4_deaths_2020['date'].apply(lambda x: getYear(x))
q4_deaths_2020 = q4_deaths_2020.groupby('Year').sum().reset_index()
q4_deaths_2020['Year'] = q4_deaths_2020['Year'].replace('2020', 'Q4')
q_deaths_2020 = q1_deaths_2020.append([q2_deaths_2020,q3_deaths_2020,q4_deaths_2020])
q_graph_2020 = pd.merge(q_cases_2020,q_deaths_2020,on='Year')
q_table_2020 = pd.merge(q_cases_2020,q_deaths_2020,on='Year')
q_table_2020.loc['Total'] = q_table_2020.sum(numeric_only=True, axis=0)
q_table_2020['Overall Cases (%)'] = (q_table_2020['Cases Positive']/q_table_2020['Cases Positive'].sum())*200
q_table_2020['Cases Vax. (%)'] = (q_table_2020['Cases Vax.']/q_table_2020['Cases Positive'])*100
q_table_2020['Recovered (%)'] = (q_table_2020['Cases Recovered']/q_table_2020['Cases Positive'])*100
q_table_2020['Death (%)'] = (q_table_2020['Death Cases']/q_table_2020['Cases Positive'])*100
q_table_2020['Deaths Vax. (%)'] = (q_table_2020['Deaths Vax.']/q_table_2020['Death Cases'])*100
q_table_2020['Year'] = q_table_2020['Year'].replace(np.nan, 'Total')
q_table_2020 = q_table_2020[['Year','Cases Positive','Overall Cases (%)',
'Cases Vax.','Cases Vax. (%)',
'Cases Recovered','Recovered (%)',
'Death Cases','Death (%)',
'Deaths Vax.','Deaths Vax. (%)']]
q_table_2020.style.set_caption("Malaysia Covid19 Quarterly Report Year 2020").set_table_styles([{
'selector': 'caption',
'props': [
('color', 'black'),
('font-size', '20px'),
("text-align", "center"),
('text-decoration', 'underline'),
('font-family','Arial'),
('text-shadow', '2px 2px 5px grey')
]},(dict
(selector='th',props=[('text-align',
'left')]))]).format(
{'Cases Positive':'{:,.0f}','Cases Recovered':'{:,.0f}','Cases Vax.':'{:,.0f}',
'Death Cases':'{:,.0f}','Deaths Vax.':'{:,.0f}','Recovered (%)':'{:,.1f}',
'Death (%)':'{:,.1f}','Cases Vax. (%)':'{:,.1f}','Deaths Vax. (%)':'{:,.1f}',
'Overall Cases (%)':'{:,.1f}'
}).apply(lambda x: ['background: salmon' if x.name in ['Total'] else '' for i in x], axis=1).hide_index()
| Year | Cases Positive | Overall Cases (%) | Cases Vax. | Cases Vax. (%) | Cases Recovered | Recovered (%) | Death Cases | Death (%) | Deaths Vax. | Deaths Vax. (%) |
|---|---|---|---|---|---|---|---|---|---|---|
| Q1 | 2,766 | 2.4 | 0 | 0.0 | 536 | 19.4 | 48 | 1.7 | 0 | 0.0 |
| Q2 | 5,873 | 5.2 | 0 | 0.0 | 7,817 | 133.1 | 73 | 1.2 | 0 | 0.0 |
| Q3 | 2,585 | 2.3 | 0 | 0.0 | 1,613 | 62.4 | 15 | 0.6 | 0 | 0.0 |
| Q4 | 101,786 | 90.1 | 0 | 0.0 | 78,974 | 77.6 | 335 | 0.3 | 0 | 0.0 |
| Total | 113,010 | 100.0 | 0 | 0.0 | 88,940 | 78.7 | 471 | 0.4 | 0 | 0.0 |
q1_cases_2021 = df_q_cases.query("date >= '2021-01-01' \
and date <= '2021-03-31'")
q2_cases_2021 = df_q_cases.query("date >= '2021-04-01' \
and date <= '2021-06-30'")
q3_cases_2021 = df_q_cases.query("date >= '2021-07-01' \
and date <= '2021-09-30'")
q4_cases_2021 = df_q_cases.query("date >= '2021-10-01' \
and date <= '2021-12-31'")
q1_deaths_2021 = df_q_deaths.query("date >= '2021-01-01' \
and date <= '2021-03-31'")
q2_deaths_2021 = df_q_deaths.query("date >= '2021-04-01' \
and date <= '2021-06-30'")
q3_deaths_2021 = df_q_deaths.query("date >= '2021-07-01' \
and date <= '2021-09-30'")
q4_deaths_2021 = df_q_deaths.query("date >= '2021-10-01' \
and date <= '2021-12-31'")
def getYear(s):
return s.split("-")[0]
q1_cases_2021['Year']= q1_cases_2021['date'].apply(lambda x: getYear(x))
q1_cases_2021 = q1_cases_2021.groupby('Year').sum().reset_index()
q1_cases_2021['Year'] = q1_cases_2021['Year'].replace('2021', 'Q1')
q2_cases_2021['Year']= q2_cases_2021['date'].apply(lambda x: getYear(x))
q2_cases_2021 = q2_cases_2021.groupby('Year').sum().reset_index()
q2_cases_2021['Year'] = q2_cases_2021['Year'].replace('2021', 'Q2')
q3_cases_2021['Year']= q3_cases_2021['date'].apply(lambda x: getYear(x))
q3_cases_2021 = q3_cases_2021.groupby('Year').sum().reset_index()
q3_cases_2021['Year'] = q3_cases_2021['Year'].replace('2021', 'Q3')
q4_cases_2021['Year']= q4_cases_2021['date'].apply(lambda x: getYear(x))
q4_cases_2021 = q4_cases_2021.groupby('Year').sum().reset_index()
q4_cases_2021['Year'] = q4_cases_2021['Year'].replace('2021', 'Q4')
q_cases_2021 = q1_cases_2021.append([q2_cases_2021,q3_cases_2021,q4_cases_2021])
q1_deaths_2021['Year']= q1_deaths_2021['date'].apply(lambda x: getYear(x))
q1_deaths_2021 = q1_deaths_2021.groupby('Year').sum().reset_index()
q1_deaths_2021['Year'] = q1_deaths_2021['Year'].replace('2021', 'Q1')
q2_deaths_2021['Year']= q2_deaths_2021['date'].apply(lambda x: getYear(x))
q2_deaths_2021 = q2_deaths_2021.groupby('Year').sum().reset_index()
q2_deaths_2021['Year'] = q2_deaths_2021['Year'].replace('2021', 'Q2')
q3_deaths_2021['Year']= q3_deaths_2021['date'].apply(lambda x: getYear(x))
q3_deaths_2021 = q3_deaths_2021.groupby('Year').sum().reset_index()
q3_deaths_2021['Year'] = q3_deaths_2021['Year'].replace('2021', 'Q3')
q4_deaths_2021['Year']= q4_deaths_2021['date'].apply(lambda x: getYear(x))
q4_deaths_2021 = q4_deaths_2021.groupby('Year').sum().reset_index()
q4_deaths_2021['Year'] = q4_deaths_2021['Year'].replace('2021', 'Q4')
q_deaths_2021 = q1_deaths_2021.append([q2_deaths_2021,q3_deaths_2021,q4_deaths_2021])
q_graph_2021 = pd.merge(q_cases_2021,q_deaths_2021,on='Year')
q_table_2021 = pd.merge(q_cases_2021,q_deaths_2021,on='Year')
q_table_2021.loc['Total'] = q_table_2021.sum(numeric_only=True, axis=0)
q_table_2021['Overall Cases (%)'] = (q_table_2021['Cases Positive']/q_table_2021['Cases Positive'].sum())*200
q_table_2021['Cases Vax. (%)'] = (q_table_2021['Cases Vax.']/q_table_2021['Cases Positive'])*100
q_table_2021['Recovered (%)'] = (q_table_2021['Cases Recovered']/q_table_2021['Cases Positive'])*100
q_table_2021['Death (%)'] = (q_table_2021['Death Cases']/q_table_2021['Cases Positive'])*100
q_table_2021['Deaths Vax. (%)'] = (q_table_2021['Deaths Vax.']/q_table_2021['Death Cases'])*100
q_table_2021['Year'] = q_table_2021['Year'].replace(np.nan, 'Total')
q_table_2021 = q_table_2021[['Year','Cases Positive','Overall Cases (%)',
'Cases Vax.','Cases Vax. (%)',
'Cases Recovered','Recovered (%)',
'Death Cases','Death (%)',
'Deaths Vax.','Deaths Vax. (%)']]
q_table_2021.style.set_caption("Malaysia Covid19 Quarterly Report Year 2021").set_table_styles([{
'selector': 'caption',
'props': [
('color', 'black'),
('font-size', '20px'),
("text-align", "center"),
('text-decoration', 'underline'),
('font-family','Arial'),
('text-shadow', '2px 2px 5px grey')
]},(dict
(selector='th',props=[('text-align',
'left')]))]).format(
{'Cases Positive':'{:,.0f}','Cases Recovered':'{:,.0f}','Cases Vax.':'{:,.0f}',
'Death Cases':'{:,.0f}','Deaths Vax.':'{:,.0f}','Recovered (%)':'{:,.1f}',
'Death (%)':'{:,.1f}','Cases Vax. (%)':'{:,.1f}','Deaths Vax. (%)':'{:,.1f}',
'Overall Cases (%)':'{:,.1f}'
}).apply(lambda x: ['background: salmon' if x.name in ['Total'] else '' for i in x], axis=1).hide_index()
| Year | Cases Positive | Overall Cases (%) | Cases Vax. | Cases Vax. (%) | Cases Recovered | Recovered (%) | Death Cases | Death (%) | Deaths Vax. | Deaths Vax. (%) |
|---|---|---|---|---|---|---|---|---|---|---|
| Q1 | 232,490 | 8.8 | 330 | 0.1 | 240,683 | 103.5 | 801 | 0.3 | 1 | 0.1 |
| Q2 | 406,479 | 15.4 | 15,759 | 3.9 | 353,056 | 86.9 | 3,898 | 1.0 | 309 | 7.9 |
| Q3 | 1,493,716 | 56.5 | 672,657 | 45.0 | 1,372,144 | 91.9 | 21,162 | 1.4 | 7,747 | 36.6 |
| Q4 | 512,391 | 19.4 | 378,417 | 73.9 | 630,555 | 123.1 | 5,155 | 1.0 | 2,768 | 53.7 |
| Total | 2,645,076 | 100.0 | 1,067,163 | 40.3 | 2,596,438 | 98.2 | 31,016 | 1.2 | 10,825 | 34.9 |
q1_cases_2022 = df_q_cases.query("date >= '2022-01-01' \
and date <= '2022-03-31'")
q2_cases_2022 = df_q_cases.query("date >= '2022-04-01' \
and date <= '2022-06-30'")
q3_cases_2022 = df_q_cases.query("date >= '2022-07-01' \
and date <= '2022-09-30'")
q4_cases_2022 = df_q_cases.query("date >= '2022-10-01' \
and date <= '2022-12-31'")
q1_deaths_2022 = df_q_deaths.query("date >= '2022-01-01' \
and date <= '2022-03-31'")
q2_deaths_2022 = df_q_deaths.query("date >= '2022-04-01' \
and date <= '2022-06-30'")
q3_deaths_2022 = df_q_deaths.query("date >= '2022-07-01' \
and date <= '2022-09-30'")
q4_deaths_2022 = df_q_deaths.query("date >= '2022-10-01' \
and date <= '2022-12-31'")
def getYear(s):
return s.split("-")[0]
q1_cases_2022['Year']= q1_cases_2022['date'].apply(lambda x: getYear(x))
q1_cases_2022 = q1_cases_2022.groupby('Year').sum().reset_index()
q1_cases_2022['Year'] = q1_cases_2022['Year'].replace('2022', 'Q1')
q2_cases_2022['Year']= q2_cases_2022['date'].apply(lambda x: getYear(x))
q2_cases_2022 = q2_cases_2022.groupby('Year').sum().reset_index()
q2_cases_2022['Year'] = q2_cases_2022['Year'].replace('2022', 'Q2')
q3_cases_2022['Year']= q3_cases_2022['date'].apply(lambda x: getYear(x))
q3_cases_2022 = q3_cases_2022.groupby('Year').sum().reset_index()
q3_cases_2022['Year'] = q3_cases_2022['Year'].replace('2022', 'Q3')
q4_cases_2022['Year']= q4_cases_2022['date'].apply(lambda x: getYear(x))
q4_cases_2022 = q4_cases_2022.groupby('Year').sum().reset_index()
q4_cases_2022['Year'] = q4_cases_2022['Year'].replace('2022', 'Q4')
q_cases_2022 = q1_cases_2022.append([q2_cases_2022,q3_cases_2022,q4_cases_2022])
q1_deaths_2022['Year']= q1_deaths_2022['date'].apply(lambda x: getYear(x))
q1_deaths_2022 = q1_deaths_2022.groupby('Year').sum().reset_index()
q1_deaths_2022['Year'] = q1_deaths_2022['Year'].replace('2022', 'Q1')
q2_deaths_2022['Year']= q2_deaths_2022['date'].apply(lambda x: getYear(x))
q2_deaths_2022 = q2_deaths_2022.groupby('Year').sum().reset_index()
q2_deaths_2022['Year'] = q2_deaths_2022['Year'].replace('2022', 'Q2')
q3_deaths_2022['Year']= q3_deaths_2022['date'].apply(lambda x: getYear(x))
q3_deaths_2022 = q3_deaths_2022.groupby('Year').sum().reset_index()
q3_deaths_2022['Year'] = q3_deaths_2022['Year'].replace('2022', 'Q3')
q4_deaths_2022['Year']= q4_deaths_2022['date'].apply(lambda x: getYear(x))
q4_deaths_2022 = q4_deaths_2022.groupby('Year').sum().reset_index()
q4_deaths_2022['Year'] = q4_deaths_2022['Year'].replace('2022', 'Q4')
q_deaths_2022 = q1_deaths_2022.append([q2_deaths_2022,q3_deaths_2022,q4_deaths_2022])
q_graph_2022 = pd.merge(q_cases_2022,q_deaths_2022,on='Year')
q_table_2022 = pd.merge(q_cases_2022,q_deaths_2022,on='Year')
q_table_2022.loc['Total'] = q_table_2022.sum(numeric_only=True, axis=0)
q_table_2022['Overall Cases (%)'] = (q_table_2022['Cases Positive']/q_table_2022['Cases Positive'].sum())*200
q_table_2022['Cases Vax. (%)'] = (q_table_2022['Cases Vax.']/q_table_2022['Cases Positive'])*100
q_table_2022['Recovered (%)'] = (q_table_2022['Cases Recovered']/q_table_2022['Cases Positive'])*100
q_table_2022['Death (%)'] = (q_table_2022['Death Cases']/q_table_2022['Cases Positive'])*100
q_table_2022['Deaths Vax. (%)'] = (q_table_2022['Deaths Vax.']/q_table_2022['Death Cases'])*100
q_table_2022['Year'] = q_table_2022['Year'].replace(np.nan, 'Total')
q_table_2022 = q_table_2022[['Year','Cases Positive','Overall Cases (%)',
'Cases Vax.','Cases Vax. (%)',
'Cases Recovered','Recovered (%)',
'Death Cases','Death (%)',
'Deaths Vax.','Deaths Vax. (%)']]
q_table_2022.style.set_caption("Malaysia Covid19 Quarterly Report Year 2022").set_table_styles([{
'selector': 'caption',
'props': [
('color', 'black'),
('font-size', '20px'),
("text-align", "center"),
('text-decoration', 'underline'),
('font-family','Arial'),
('text-shadow', '2px 2px 5px grey')
]},(dict
(selector='th',props=[('text-align',
'left')]))]).format(
{'Cases Positive':'{:,.0f}','Cases Recovered':'{:,.0f}','Cases Vax.':'{:,.0f}',
'Death Cases':'{:,.0f}','Deaths Vax.':'{:,.0f}','Recovered (%)':'{:,.1f}',
'Death (%)':'{:,.1f}','Cases Vax. (%)':'{:,.1f}','Deaths Vax. (%)':'{:,.1f}',
'Overall Cases (%)':'{:,.1f}'
}).apply(lambda x: ['background: salmon' if x.name in ['Total'] else '' for i in x], axis=1).hide_index()
| Year | Cases Positive | Overall Cases (%) | Cases Vax. | Cases Vax. (%) | Cases Recovered | Recovered (%) | Death Cases | Death (%) | Deaths Vax. | Deaths Vax. (%) |
|---|---|---|---|---|---|---|---|---|---|---|
| Q1 | 1,443,833 | 87.4 | 1,207,925 | 83.7 | 1,274,704 | 88.3 | 3,496 | 0.2 | 2,383 | 68.2 |
| Q2 | 207,283 | 12.6 | 177,529 | 85.6 | 322,931 | 155.8 | 482 | 0.2 | 225 | 46.7 |
| Total | 1,651,116 | 100.0 | 1,385,454 | 83.9 | 1,597,635 | 96.8 | 3,978 | 0.2 | 2,608 | 65.6 |
fig = make_subplots(rows=1, cols=3, specs=[[{'secondary_y': True}, {'secondary_y': True},
{'secondary_y': True}]],
subplot_titles=('<b>Year 2020</b>',
'<b>Year 2021</b>',
'<b>Year 2022</b>'))
#Graph 1
fig.append_trace(go.Bar(x = q_graph_2020['Year'], y = q_graph_2020['Cases Positive'],
name='Cases Positive',marker_color='red'),row=1, col=1)
fig.append_trace(go.Bar(x = q_graph_2020['Year'], y = q_graph_2020['Cases Recovered'],
name='Cases Recovered',marker_color='blue'),row=1, col=1)
#Graph 2
fig.append_trace(go.Bar(x = q_graph_2021['Year'], y = q_graph_2021['Cases Positive'],
name='Cases Positive',marker_color='red'),row=1, col=2)
fig.append_trace(go.Bar(x = q_graph_2021['Year'], y = q_graph_2021['Cases Recovered'],
name='Cases Recovered',marker_color='blue'),row=1, col=2)
#Graph 3
fig.append_trace(go.Bar(x = q_graph_2022['Year'], y = q_graph_2022['Cases Positive'],
name='Cases Positive',marker_color='red'),row=1, col=3)
fig.append_trace(go.Bar(x = q_graph_2022['Year'], y = q_graph_2022['Cases Recovered'],
name='Cases Recovered',marker_color='blue'),row=1, col=3)
#Update Fonts & Size
fig.update_annotations(font=dict(family="Helvetica", size=11))
fig.update_layout(font=dict(family="Helvetica", size=11))
fig.update_layout(height=400,showlegend=False)
fig.update_layout(height=400,showlegend=False,title_text='Covid19 Quarterly Report: Positive VS Recovered Cases', title_x=0.5)
fig.update_xaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
fig.update_yaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
#Plotting the graph
fig.show()
fig = make_subplots(rows=1, cols=3, specs=[[{'secondary_y': True}, {'secondary_y': True},
{'secondary_y': True}]],
subplot_titles=('<b>Year 2020</b>',
'<b>Year 2021</b>',
'<b>Year 2022</b>'))
#Graph 1
fig.append_trace(go.Bar(x = q_graph_2020['Year'], y = q_graph_2020['Death Cases'],
name='Death Cases',marker_color='red'),row=1, col=1)
fig.append_trace(go.Bar(x = q_graph_2020['Year'], y = q_graph_2020['Deaths Vax.'],
name='Deaths Vax.',marker_color='blue'),row=1, col=1)
#Graph 2
fig.append_trace(go.Bar(x = q_graph_2021['Year'], y = q_graph_2021['Death Cases'],
name='Death Cases',marker_color='red'),row=1, col=2)
fig.append_trace(go.Bar(x = q_table_2021['Year'], y = q_graph_2021['Deaths Vax.'],
name='Deaths Vax.',marker_color='blue'),row=1, col=2)
#Graph 3
fig.append_trace(go.Bar(x = q_graph_2022['Year'], y = q_graph_2022['Death Cases'],
name='Death Cases',marker_color='red'),row=1, col=3)
fig.append_trace(go.Bar(x = q_graph_2022['Year'], y = q_graph_2022['Deaths Vax.'],
name='Deaths Vax.',marker_color='blue'),row=1, col=3)
#Update Fonts & Size
fig.update_annotations(font=dict(family="Helvetica", size=11))
fig.update_layout(font=dict(family="Helvetica", size=11))
fig.update_layout(height=400,showlegend=False)
fig.update_layout(height=400,showlegend=False,title_text='Covid19 Quarterly Report: Deaths Cases New VS Vax.', title_x=0.5)
fig.update_xaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
fig.update_yaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
#Plotting the graph
fig.show()
fig = make_subplots(shared_xaxes=True, specs=[[{'secondary_y': True}]])
#Graph 1
fig.add_trace(go.Scatter(x = df_mas_cases['date'], y = df_mas_cases['cases_new'], name ='Positive Cases',
line = dict(color='blue', width=1)), secondary_y=True, row=1, col=1)
fig.add_trace(go.Scatter(x = df_mas_cases['date'], y = df_mas_cases['cases_recovered'], name = 'Recovered Cases',
line = dict(color='red', width=1)), secondary_y=True, row=1, col=1)
fig.add_trace(go.Scatter(x = df_mas_cases['date'], y = df_mas_cases['total_vax'], name = 'Vax. Cases',
line = dict(color='green', width=1)), secondary_y=True, row=1, col=1)
fig.add_trace(go.Scatter(x = df_mas_cases['date'], y = df_mas_cases['cum_new'], name ='Cumulative Cases',
line = dict(color='blue', width=1)), secondary_y=False, row=1, col=1)
fig.add_trace(go.Scatter(x = df_mas_cases['date'], y = df_mas_cases['cum_recovered'], name = 'Cumulative Recovered',
line = dict(color='red', width=1)), secondary_y=False, row=1, col=1)
fig.add_trace(go.Scatter(x = df_mas_cases['date'], y = df_mas_cases['cum_vax'], name = 'Cumulative Vax.',
line = dict(color='green', width=1)), secondary_y=False, row=1, col=1)
fig.update_layout(title_text='Malaysia Covid19 New And Recovered Cases', title_x=0.5, showlegend=False,
height=600)
fig.update_xaxes(title_text='')
fig.update_annotations(font=dict(family="Helvetica", size=12))
fig.update_layout(font=dict(family="Helvetica", size=14))
fig.update_xaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
fig.update_yaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
#fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
#x0='2021-10-11', x1='2021-10-11', y0=0, y1=1000000)
#fig.add_annotation(text='90% Vax.', x='2021-10-11', y=1000000, arrowhead=3, align='center',
#arrowsize=1, arrowwidth=2, arrowcolor="#636363",xref='x', ax=50, ay=-90, showarrow=True,
#xanchor="left", yanchor="bottom")
#fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
#x0='2020-09-26', x1='2020-09-26', y0=0, y1=500000)
#fig.add_annotation(text='PRN Sabah', x='2020-09-26', y=500000, arrowhead=3, align='center',
#arrowsize=1, arrowwidth=2, arrowcolor="#636363",xref='x', ax=50, ay=-30, showarrow=True,
#xanchor="left", yanchor="bottom")
#fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
#x0='2021-11-20', x1='2021-11-20', y0=0, y1=500000)
#fig.add_annotation(text='PRN Melaka', x='2021-11-20', y=500000, arrowhead=3, align='center',
#arrowsize=1, arrowwidth=2, arrowcolor="#636363",xref='x', ax=50, ay=-30, showarrow=True,
#xanchor="left", yanchor="bottom")
#fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
#x0='2021-12-06', x1='2021-12-06', y0=0, y1=400000)
#fig.add_annotation(text='PRN Sarawak', x='2021-12-06', y=400000, arrowhead=3, align='center',
#arrowsize=1, arrowwidth=2, arrowcolor="#636363",xref='x', ax=50, ay=-30, showarrow=True,
#xanchor="left", yanchor="bottom")
#fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
#x0='2021-01-12', x1='2021-01-12', y0=0, y1=600000)
#fig.add_annotation(text='Darurat Mula', x='2021-01-12', y=600000, arrowhead=3, align='center',
#arrowsize=1, arrowwidth=2, arrowcolor="#636363",xref='x', ax=50, ay=-30, showarrow=True,
#xanchor="left", yanchor="bottom")
#fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
#x0='2021-08-01', x1='2021-08-01', y0=0, y1=1100000)
#fig.add_annotation(text='Darurat Tamat', x='2021-08-01', y=1100000, arrowhead=3, align='center',
#arrowsize=1, arrowwidth=2, arrowcolor="#636363",xref='x', ax=50, ay=-30, showarrow=True,
#xanchor="left", yanchor="bottom")
fig.show()
fig = make_subplots(shared_xaxes=True, specs=[[{'secondary_y': True}]])
#Graph 1
fig.add_trace(go.Scatter(x = df_mas_cases['date'], y = df_mas_deaths['deaths_new'], name ='Death Cases',
line = dict(color='blue', width=1)), secondary_y=True, row=1, col=1)
fig.add_trace(go.Scatter(x = df_mas_cases['date'], y = df_mas_deaths['total_vax'], name = 'Vax Death Cases',
line = dict(color='red', width=1)), secondary_y=True, row=1, col=1)
fig.add_trace(go.Scatter(x = df_mas_cases['date'], y = df_mas_deaths['cum_deaths'], name ='Cum. Death Cases',
line = dict(color='blue', width=1)), secondary_y=False, row=1, col=1)
fig.add_trace(go.Scatter(x = df_mas_cases['date'], y = df_mas_deaths['cum_vax'], name = 'Cum. Vax Death Recovered',
line = dict(color='red', width=1)), secondary_y=False, row=1, col=1)
fig.update_layout(title_text='Malaysia Covid19 Death Cases: New VS Vax', title_x=0.5, showlegend=False,
height=600)
fig.update_xaxes(title_text='')
fig.update_annotations(font=dict(family="Helvetica", size=12))
fig.update_layout(font=dict(family="Helvetica", size=14))
fig.update_xaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
fig.update_yaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
fig.show()
new_cases = df_mas_cases.tail(1)
new_deaths = df_mas_deaths.tail(1)
#Creating the main key points from the datasets
print('Key Points Highlights:-')
print('-----------------------------------------------------------------')
print('1. Total Positive Cases :','{0:,.0f}'.format(df_states_table.at[df_states_table.index[16],'Cases Positive']),'/','{0:.1f}'.format(df_states_table.at[df_states_table.index[16],'Cases (%)']),'%')
print('2. New Positive Cases :','{0:,.0f}'.format(new_cases.at[new_cases.index[0],'cases_new']))
print('3. Total Positive Cases After Vaccinated :','{0:,.0f}'.format(df_states_table.at[df_states_table.index[16],'Cases Vax.']),'/','{0:.1f}'.format(df_states_table.at[df_states_table.index[16],'Cases Vax. (%)']),'%')
print('4. Total Recovered Cases :','{0:,.0f}'.format(df_states_table.at[df_states_table.index[16],'Cases Recovered']),'/','{0:.1f}'.format(df_states_table.at[df_states_table.index[16],'Cases Recovered (%)']),'%')
print('5. New Recovered Cases :','{0:,.0f}'.format(new_cases.at[new_cases.index[0],'cases_recovered']))
print('6. Total Death Cases :','{0:,.0f}'.format(df_states_table.at[df_states_table.index[16],'Death Cases']),'/','{0:.1f}'.format(df_states_table.at[df_states_table.index[16],'Deaths (%)']),'%')
print('7. New Death Cases :','{0:,.0f}'.format(new_deaths.at[new_deaths.index[0],'deaths_new']))
print('8. Total Death Cases After Vaccinated :','{0:,.0f}'.format(df_states_table.at[df_states_table.index[16],'Deaths Vax.']),'/','{0:.1f}'.format(df_states_table.at[df_states_table.index[16],'Deaths Vax. (%)']),'%')
print('9. Total 1 Dose Vaccinated :','{0:,.0f}'.format(df_states_table.at[df_states_table.index[16],'daily_partial']),'/','{0:.1f}'.format(df_states_table.at[df_states_table.index[16],'Vax. 1D (%)']),'%')
print('10. Total 2 Dose Vaccinated :','{0:,.0f}'.format(df_states_table.at[df_states_table.index[16],'daily_full']),'/','{0:.1f}'.format(df_states_table.at[df_states_table.index[16],'Vax. 2D (%)']),'%')
print('11. Total 3 Dose Vaccinated :','{0:,.0f}'.format(df_states_table.at[df_states_table.index[16],'daily_booster']),'/','{0:.1f}'.format(df_states_table.at[df_states_table.index[16],'Vax. 3D (%)']),'%')
print('-----------------------------------------------------------------')
Key Points Highlights:- ----------------------------------------------------------------- 1. Total Positive Cases : 4,409,202 / 13.5 % 2. New Positive Cases : 6,968 3. Total Positive Cases After Vaccinated : 2,452,617 / 55.6 % 4. Total Recovered Cases : 4,283,013 / 97.1 % 5. New Recovered Cases : 8,267 6. Total Death Cases : 35,465 / 0.8 % 7. New Death Cases : 16 8. Total Death Cases After Vaccinated : 13,433 / 37.9 % 9. Total 1 Dose Vaccinated : 27,430,337 / 84.0 % 10. Total 2 Dose Vaccinated : 26,327,040 / 80.6 % 11. Total 3 Dose Vaccinated : 15,985,545 / 48.9 % -----------------------------------------------------------------
df_states_table.drop(['daily_partial','daily_full','pop','daily','daily_booster'],axis='columns',inplace=True)
#df_states_table.drop(['Cases Vax.','Death BID','Deaths Vax.'],axis='columns',inplace=True)
df_states_table['Overall Cases (%)'] = (df_states_table['Cases Positive']/df_states_table['Cases Positive'].sum())*200
df_states_table = df_states_table[['States','Cases Positive','Overall Cases (%)',
'Cases (%)','Cases Vax. (%)',
'Cases Recovered','Cases Recovered (%)',
'Death Cases','Deaths (%)',
'Deaths Vax. (%)','Deaths BID (%)',
'Vax. 3D (%)','Vax. 2D (%)','Vax. 1D (%)']]
df_states_table = df_states_table.sort_values('Cases Positive',ascending=False)
df_states_table.style.set_caption("Malaysia Details Of Covid19 Cases At Each States").set_table_styles([{
'selector': 'caption',
'props': [
('color', 'black'),
('font-size', '26px'),
("text-align", "center"),
('text-decoration', 'underline'),
('font-family','Arial'),
('text-shadow', '2px 2px 5px grey')
]},(dict
(selector='th',props=[('text-align',
'left')]))]).format(
{'Cases Positive':'{:,.0f}','Cases Recovered':'{:,.0f}','Cases Vax.':'{:,.0f}',
'Death Cases':'{:,.0f}','Deaths (%)':'{:,.1f}','Deaths BID':'{:,.0f}',
'Deaths Vax.':'{:,.0f}','Cases (%)':'{:,.1f}','Cases Recovered (%)':'{:,.1f}',
'Cases Vax. (%)':'{:,.1f}','Vax. 2D (%)':'{:,.1f}','Vax. 1D (%)':'{:,.1f}',
'Deaths Vax. (%)':'{:,.1f}','Deaths BID (%)':'{:,.1f}','Vax. 3D (%)':'{:,.1f}',
'Overall Cases (%)':'{:,.1f}'}
).set_properties(subset=['States'],**{'text-align': 'left'}).apply(
lambda x: ['background: salmon' if x.name in ['Total'] else '' for i in x], axis=1).hide_index()
| States | Cases Positive | Overall Cases (%) | Cases (%) | Cases Vax. (%) | Cases Recovered | Cases Recovered (%) | Death Cases | Deaths (%) | Deaths Vax. (%) | Deaths BID (%) | Vax. 3D (%) | Vax. 2D (%) | Vax. 1D (%) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Malaysia | 4,409,202 | 100.0 | 13.5 | 55.6 | 4,283,013 | 97.1 | 35,465 | 0.8 | 37.9 | 21.2 | 48.9 | 80.6 | 84.0 |
| Selangor | 1,318,085 | 29.9 | 20.1 | 55.5 | 1,255,698 | 95.3 | 10,607 | 0.8 | 34.7 | 21.6 | 61.4 | 74.9 | 78.5 |
| Johor | 380,942 | 8.6 | 10.0 | 52.6 | 372,045 | 97.7 | 4,497 | 1.2 | 42.4 | 13.6 | 55.6 | 82.5 | 86.8 |
| Sabah | 370,132 | 8.4 | 9.7 | 51.9 | 358,640 | 96.9 | 3,120 | 0.8 | 26.2 | 40.7 | 23.7 | 63.6 | 63.7 |
| W.P. Kuala Lumpur | 343,469 | 7.8 | 19.7 | 54.8 | 333,769 | 97.2 | 2,814 | 0.8 | 36.0 | 26.0 | 98.3 | 176.7 | 184.5 |
| Sarawak | 304,281 | 6.9 | 10.8 | 59.8 | 300,936 | 98.9 | 1,725 | 0.6 | 50.9 | 21.8 | 55.4 | 78.8 | 85.6 |
| Kedah | 297,680 | 6.8 | 13.6 | 57.2 | 293,711 | 98.7 | 2,615 | 0.9 | 38.7 | 17.9 | 33.5 | 73.3 | 75.7 |
| Pulau Pinang | 275,534 | 6.2 | 15.5 | 60.7 | 271,947 | 98.7 | 1,976 | 0.7 | 42.5 | 21.6 | 60.3 | 88.3 | 91.1 |
| Kelantan | 247,112 | 5.6 | 12.8 | 55.1 | 245,072 | 99.2 | 1,419 | 0.6 | 36.0 | 28.0 | 16.9 | 62.0 | 63.7 |
| Negeri Sembilan | 199,492 | 4.5 | 17.7 | 52.1 | 193,715 | 97.1 | 1,471 | 0.7 | 32.6 | 12.6 | 58.0 | 86.1 | 89.9 |
| Perak | 198,691 | 4.5 | 7.9 | 54.4 | 193,311 | 97.3 | 1,905 | 1.0 | 49.8 | 15.5 | 45.9 | 76.4 | 79.0 |
| Pahang | 170,202 | 3.9 | 10.1 | 58.5 | 167,814 | 98.6 | 968 | 0.6 | 40.3 | 13.2 | 36.8 | 71.5 | 74.1 |
| Terengganu | 125,360 | 2.8 | 9.8 | 58.0 | 122,902 | 98.0 | 859 | 0.7 | 44.5 | 12.8 | 27.4 | 70.0 | 71.9 |
| Melaka | 120,938 | 2.7 | 12.9 | 53.5 | 117,184 | 96.9 | 1,110 | 0.9 | 41.3 | 14.6 | 57.6 | 79.6 | 82.7 |
| W.P. Labuan | 21,251 | 0.5 | 21.2 | 48.5 | 21,046 | 99.0 | 155 | 0.7 | 11.0 | 28.4 | 49.3 | 81.0 | 88.2 |
| W.P. Putrajaya | 18,844 | 0.4 | 16.2 | 61.8 | 18,361 | 97.4 | 42 | 0.2 | 52.4 | 2.4 | 71.0 | 129.1 | 139.3 |
| Perlis | 17,189 | 0.4 | 6.7 | 71.9 | 16,862 | 98.1 | 182 | 1.1 | 46.7 | 4.4 | 30.7 | 81.5 | 84.0 |
df_states_graph = df_states
df_states_graph['vaksin_percentage_partial'] = df_states_graph['daily_partial']/df_states_graph['pop']*100
df_states_graph['vaksin_percentage_full'] = df_states_graph['daily_full']/df_states_graph['pop']*100
df_states_graph['deaths_vax_total'] = df_states_graph['deaths_pvax'] + df_states_graph['deaths_fvax']
df_states_graph['cases_vax_total'] = df_states_graph['cases_pvax'] + df_states_graph['cases_fvax']
df_mas_deaths['deaths_vax_total'] = df_mas_deaths['deaths_pvax'] + df_mas_deaths['deaths_fvax'] + df_mas_deaths['deaths_boost']
df_mas['deaths_vax_total'] = df_mas['deaths_pvax'] + df_mas['deaths_fvax'] + df_mas['deaths_boost']
df_mas_test['total_test'] = df_mas_test['rtk-ag'] + df_mas_test['pcr']
df_mas_test['cum_total'] = df_mas_test['total_test'].cumsum()
df_mas_new = df_mas[['date','cases_new','deaths_new']]
df_mas_new['cases_new_7'] = df_mas_new['cases_new'].shift(7)
df_mas_new['cases_new_14'] = df_mas_new['cases_new'].shift(14)
df_mas_new['cases_new_21'] = df_mas_new['cases_new'].shift(21)
df_mas_new['deaths_new_7'] = df_mas_new['deaths_new'].shift(7)
df_mas_new['deaths_new_14'] = df_mas_new['deaths_new'].shift(14)
df_mas_new['deaths_new_21'] = df_mas_new['deaths_new'].shift(21)
df_mas_new = df_mas_new.tail(7)
df_mas_new['cum_new'] = df_mas_new['cases_new'].cumsum()
df_mas_new['cum_new_7'] = df_mas_new['cases_new_7'].cumsum()
df_mas_new['cum_new_14'] = df_mas_new['cases_new_14'].cumsum()
df_mas_new['cum_new_21'] = df_mas_new['cases_new_21'].cumsum()
df_mas_new['cum_deaths_new'] = df_mas_new['deaths_new'].cumsum()
df_mas_new['cum_deaths_7'] = df_mas_new['deaths_new_7'].cumsum()
df_mas_new['cum_deaths_14'] = df_mas_new['deaths_new_14'].cumsum()
df_mas_new['cum_deaths_21'] = df_mas_new['deaths_new_21'].cumsum()
print('Four Weeks Cumulative Daily Cases Comparison Analysis:-')
print('--------------------------------------------')
print('Average Week 1: ', '{0:,.0f}'.format(df_mas_new['cum_new'].mean()), '(', '{0:,.0f}'.format(df_mas_new['cum_new'].mean()-df_mas_new['cum_new_7'].mean()),'diff.)')
print('Average Week 2: ', '{0:,.0f}'.format(df_mas_new['cum_new_7'].mean()), '(', '{0:,.0f}'.format(df_mas_new['cum_new_7'].mean()-df_mas_new['cum_new_14'].mean()),'diff.)')
print('Average Week 3: ', '{0:,.0f}'.format(df_mas_new['cum_new_14'].mean()), '(', '{0:,.0f}'.format(df_mas_new['cum_new_14'].mean()-df_mas_new['cum_new_21'].mean()),'diff.)')
print('Average Week 4: ', '{0:,.0f}'.format(df_mas_new['cum_new_21'].mean()), '-')
print('--------------------------------------------')
print('Four Weeks Daily Cases Comparison Analysis:-')
print('--------------------------------------------')
print('Average Week 1:', '{0:,.0f}'.format(df_mas_new['cases_new'].mean()), '(', '{0:,.0f}'.format(df_mas_new['cases_new'].mean()-df_mas_new['cases_new_7'].mean()),'diff.)')
print('Average Week 2:', '{0:,.0f}'.format(df_mas_new['cases_new_7'].mean()), '(', '{0:,.0f}'.format(df_mas_new['cases_new_7'].mean()-df_mas_new['cases_new_14'].mean()),'diff.)')
print('Average Week 3:', '{0:,.0f}'.format(df_mas_new['cases_new_14'].mean()), '(', '{0:,.0f}'.format(df_mas_new['cases_new_14'].mean()-df_mas_new['cases_new_21'].mean()),'diff.)')
print('Average Week 4:', '{0:,.0f}'.format(df_mas_new['cases_new_21'].mean()), '-')
print('--------------------------------------------')
Four Weeks Cumulative Daily Cases Comparison Analysis:- -------------------------------------------- Average Week 1: 37,178 ( -8,671 diff.) Average Week 2: 45,849 ( -16,759 diff.) Average Week 3: 62,608 ( -21,491 diff.) Average Week 4: 84,099 - -------------------------------------------- Four Weeks Daily Cases Comparison Analysis:- -------------------------------------------- Average Week 1: 8,525 ( -2,057 diff.) Average Week 2: 10,582 ( -3,856 diff.) Average Week 3: 14,438 ( -4,845 diff.) Average Week 4: 19,283 - --------------------------------------------
fig = make_subplots(rows=1, cols=4, shared_xaxes=True, vertical_spacing=0.08,
subplot_titles=('<b>New Confirmed Cases Daily</b>',
'<b>New Confirmed Cases Cumulative</b>',
'<b>New Deaths Cases Daily</b>',
'<b>New Deaths Cases Cumulative</b>'))
#Graph 1
fig.append_trace(go.Scatter(x = df_mas_new['date'], y = df_mas_new['cases_new'],name='Week 1', mode="lines",
line = dict(color='red',width=0.5)),row=1, col=1)
fig.append_trace(go.Scatter(x = df_mas_new['date'], y = df_mas_new['cases_new_7'],name='Week 2', mode="lines",
line = dict(color='blue',width=0.5)),row=1, col=1)
fig.append_trace(go.Scatter(x = df_mas_new['date'], y = df_mas_new['cases_new_14'],name='Week 3', mode="lines",
line = dict(color='green',width=0.5)),row=1, col=1)
fig.append_trace(go.Scatter(x = df_mas_new['date'], y = df_mas_new['cases_new_21'],name='Week 4', mode="lines",
line = dict(color='purple',width=0.5)),row=1, col=1)
fig.add_hline(y=df_mas_new['cases_new'].mean(), line_dash="dot",line_color="red",
annotation_text="Ave. Week 1", annotation_position="bottom left",row=1, col=1)
fig.add_hline(y=df_mas_new['cases_new_7'].mean(), line_dash="dot",line_color="blue",
annotation_text="Ave. Week 2", annotation_position="bottom right",row=1, col=1)
fig.add_hline(y=df_mas_new['cases_new_14'].mean(), line_dash="dot",line_color="green",
annotation_text="Ave. Week 3", annotation_position="bottom left",row=1, col=1)
fig.add_hline(y=df_mas_new['cases_new_21'].mean(), line_dash="dot",line_color="purple",
annotation_text="Ave. Week 4", annotation_position="bottom right",row=1, col=1)
#Graph 2
fig.append_trace(go.Scatter(x = df_mas_new['date'], y = df_mas_new['cum_new'],name='Week 1', mode="lines",
line = dict(color='red',width=0.5)),row=1, col=2)
fig.append_trace(go.Scatter(x = df_mas_new['date'], y = df_mas_new['cum_new_7'],name='Week 2', mode="lines",
line = dict(color='blue',width=0.5)),row=1, col=2)
fig.append_trace(go.Scatter(x = df_mas_new['date'], y = df_mas_new['cum_new_14'],name='Week 3', mode="lines",
line = dict(color='green',width=0.5)),row=1, col=2)
fig.append_trace(go.Scatter(x = df_mas_new['date'], y = df_mas_new['cum_new_21'],name='Week 4', mode="lines",
line = dict(color='purple',width=0.5)),row=1, col=2)
fig.add_hline(y=df_mas_new['cum_new'].mean(), line_dash="dot",line_color="red",
annotation_text="Ave. Week 1", annotation_position="bottom left",row=1, col=2)
fig.add_hline(y=df_mas_new['cum_new_7'].mean(), line_dash="dot",line_color="blue",
annotation_text="Ave. Week 2", annotation_position="bottom right",row=1, col=2)
fig.add_hline(y=df_mas_new['cum_new_14'].mean(), line_dash="dot",line_color="green",
annotation_text="Ave. Week 3", annotation_position="top left",row=1, col=2)
fig.add_hline(y=df_mas_new['cum_new_21'].mean(), line_dash="dot",line_color="purple",
annotation_text="Ave. Week 4", annotation_position="bottom right",row=1, col=2)
#Graph 3
fig.append_trace(go.Scatter(x = df_mas_new['date'], y = df_mas_new['deaths_new'],name='Week 1', mode="lines",
line = dict(color='red', width=0.5)),row=1, col=3)
fig.append_trace(go.Scatter(x = df_mas_new['date'], y = df_mas_new['deaths_new_7'],name='Week 2', mode="lines",
line = dict(color='blue', width=0.5)),row=1, col=3)
fig.append_trace(go.Scatter(x = df_mas_new['date'], y = df_mas_new['deaths_new_14'],name='Week 3', mode="lines",
line = dict(color='green', width=0.5)),row=1, col=3)
fig.append_trace(go.Scatter(x = df_mas_new['date'], y = df_mas_new['deaths_new_21'],name='Week 4', mode="lines",
line = dict(color='purple', width=0.5)),row=1, col=3)
fig.add_hline(y=df_mas_new['deaths_new'].mean(), line_dash="dot",line_color="red",
annotation_text="Ave. Week 1", annotation_position="bottom left",row=1, col=3)
fig.add_hline(y=df_mas_new['deaths_new_7'].mean(), line_dash="dot",line_color="blue",
annotation_text="Ave. Week 2", annotation_position="bottom right",row=1, col=3)
fig.add_hline(y=df_mas_new['deaths_new_14'].mean(), line_dash="dot",line_color="green",
annotation_text="Ave. Week 3", annotation_position="bottom left",row=1, col=3)
fig.add_hline(y=df_mas_new['deaths_new_21'].mean(), line_dash="dot",line_color="purple",
annotation_text="Ave. Week 4", annotation_position="bottom right",row=1, col=3)
#Graph 4
fig.append_trace(go.Scatter(x = df_mas_new['date'], y = df_mas_new['cum_deaths_new'],name='Week 1', mode="lines",
line = dict(color='red', width=0.5)),row=1, col=4)
fig.append_trace(go.Scatter(x = df_mas_new['date'], y = df_mas_new['cum_deaths_7'],name='Week 2', mode="lines",
line = dict(color='blue', width=0.5)),row=1, col=4)
fig.append_trace(go.Scatter(x = df_mas_new['date'], y = df_mas_new['cum_deaths_14'],name='Week 3', mode="lines",
line = dict(color='green', width=0.5)),row=1, col=4)
fig.append_trace(go.Scatter(x = df_mas_new['date'], y = df_mas_new['cum_deaths_21'],name='Week 4', mode="lines",
line = dict(color='purple', width=0.5)),row=1, col=4)
fig.add_hline(y=df_mas_new['cum_deaths_new'].mean(), line_dash="dot",line_color="red",
annotation_text="Ave. Week 1", annotation_position="bottom left",row=1, col=4)
fig.add_hline(y=df_mas_new['cum_deaths_7'].mean(), line_dash="dot",line_color="blue",
annotation_text="Ave. Week 2", annotation_position="bottom right",row=1, col=4)
fig.add_hline(y=df_mas_new['cum_deaths_14'].mean(), line_dash="dot",line_color="green",
annotation_text="Ave. Week 3", annotation_position="bottom left",row=1, col=4)
fig.add_hline(y=df_mas_new['cum_deaths_21'].mean(), line_dash="dot",line_color="purple",
annotation_text="Ave. Week 4", annotation_position="top right",row=1, col=4)
#Update Fonts & Size
fig.update_annotations(font=dict(family="Helvetica", size=11))
fig.update_layout(font=dict(family="Helvetica", size=11))
fig.update_layout(height=400,showlegend=False,title_text="Malaysia Covid19 Latest Cases In Details (4 Week Comparison)", title_x=0.5)
fig.update_xaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black',
visible=True, showticklabels=False)
fig.update_yaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
#Plotting the graph
fig.show()
df_mas_cases['cum_new'] = df_mas_cases['cases_new'].cumsum()
df_mas_cases['cum_recovered'] = df_mas_cases['cases_recovered'].cumsum()
df_mas_deaths['cum_deaths'] = df_mas_deaths['deaths_new'].cumsum()
df_mas_deaths['cum_deaths_vax'] = df_mas_deaths['deaths_vax_total'].cumsum()
fig = make_subplots(rows=2, cols=4, shared_xaxes=True, vertical_spacing=0.08,
specs=[[{'secondary_y': True}, {'secondary_y': True}, {'secondary_y': True}, {'secondary_y': True}],
[{'secondary_y': True}, {'secondary_y': True}, {'secondary_y': True}, {'secondary_y': True}]],
subplot_titles=('<b>Cases: Total VS Recover</b>',
'<b>ICU Daily Admitted</b>',
'<b>Hosp.: Admit VS Discharge</b>',
'<b>PKRC: Admitted VS Discharged</b>',
'<b>Deaths: Total, BID & Vax.</b>',
'<b>Total Daily Covid19 Test</b>',
'<b>States Hosp. Admission & Discharge</b>',
'<b>States Vax. (%): 1D VS 2D</b>'))
#Graph 1
fig.add_trace(go.Scatter(x = df_mas_cases['date'], y = df_mas_cases['cases_new'], name='Positive Cases',
line = dict(color='red',width=0.5)), row=1, col=1, secondary_y=True)
fig.add_trace(go.Scatter(x = df_mas_cases['date'], y = df_mas_cases['cum_new'], name='Positive Cases',
line = dict(color='red',width=1)), row=1, col=1, secondary_y=False)
fig.add_trace(go.Scatter(x = df_mas_cases['date'], y = df_mas_cases['cases_recovered'],name='Recovered Cases',
line = dict(color='blue',width=0.5)),row=1, col=1, secondary_y=True)
fig.add_trace(go.Scatter(x = df_mas_cases['date'], y = df_mas_cases['cum_recovered'],name='Recovered Cases',
line = dict(color='blue',width=1)),row=1, col=1, secondary_y=False)
fig.add_hline(y=df_mas_cases['cases_new'].mean(), line_dash="dot",line_color="red",
annotation_text="Ave. Cases", annotation_position="bottom left",row=1, col=1)
fig.add_hline(y=df_mas_cases['cases_recovered'].mean(), line_dash="dot",line_color="blue",
annotation_text="Ave. Recovered", annotation_position="top left",row=1, col=1)
#Graph 2
fig.add_trace(go.Scatter(x = df_icu_daily['date'], y = df_icu_daily['icu_covid'],name='ICU Covid',
line = dict(color='blue', width=0.5)),row=1, col=2, secondary_y=True)
fig.add_trace(go.Scatter(x = df_icu_daily['date'], y = df_icu_daily['icu_covid_cum'],name='ICU Covid',
line = dict(color='red', width=0.5)),row=1, col=2, secondary_y=False)
fig.add_hline(y = df_icu_daily['icu_covid'].mean(), line_dash="dot",line_color="blue",
annotation_text="Ave. Daily", annotation_position="bottom left",row=1, col=2)
fig.add_hline(y = df_icu_daily['icu_covid_cum'].mean(), line_dash="dot",line_color="red",
annotation_text="Ave. Cum.", annotation_position="bottom left",row=1, col=2)
# Graph 3
fig.add_trace(go.Scatter(x = df_mas_deaths['date'], y = df_mas_deaths['deaths_new'],name='Deaths Cases',
line = dict(width=0.5, color='blue')),row=2, col=1, secondary_y=True)
fig.add_trace(go.Scatter(x = df_mas_deaths['date'], y = df_mas_deaths['cum_deaths'],name='Deaths Cases',
line = dict(width=1, color='blue')),row=2, col=1, secondary_y=False)
fig.add_hline(y=df_mas_deaths['deaths_new'].mean(), line_dash="dot",line_color="blue",
annotation_text="Ave. Daily Deaths", annotation_position="top left",row=2, col=1)
fig.add_trace(go.Scatter(x = df_mas_deaths['date'], y = df_mas_deaths['deaths_bid_dod'],name='BID Cases',
line = dict(width=0.5, color='red')),row=2, col=1, secondary_y=True)
fig.add_trace(go.Scatter(x = df_mas_deaths['date'], y = df_mas_deaths['deaths_vax_total'],name='Deaths Vaccinated Cases',
line = dict(width=0.5, color='black')),row=2, col=1, secondary_y=True)
fig.add_trace(go.Scatter(x = df_mas_deaths['date'], y = df_mas_deaths['cum_deaths_vax'],name='Deaths Vaccinated Cases',
line = dict(width=1, color='black')),row=2, col=1, secondary_y=False)
#Graph 4
fig.add_trace(go.Scatter(x = df_mas_test['date'], y = df_mas_test['total_test'],name='Daily Covid19 Test',
line = dict(color='blue', width=0.5)),row=2, col=2, secondary_y=False)
fig.add_trace(go.Scatter(x = df_mas_test['date'], y = df_mas_test['cum_total'],name='Cumulative Covid19 Test',
line = dict(color='red', width=1)),row=2, col=2, secondary_y=True)
fig.add_hline(y=df_mas_test['total_test'].mean(), line_dash="dot",line_color="blue",
annotation_text="Ave. Daily Test", annotation_position="top left",row=2, col=2)
#Graph 5
fig.add_trace(go.Scatter(x = df_hosp_daily['date'], y = df_hosp_daily['admitted_covid'],name='Admitted',
line = dict(color='blue', width=0.5)),row=1, col=3, secondary_y=True)
fig.add_trace(go.Scatter(x = df_hosp_daily['date'], y = df_hosp_daily['discharged_covid'],name='Discharged',
line = dict(color='red', width=0.5)),row=1, col=3, secondary_y=True)
fig.add_trace(go.Scatter(x = df_hosp_daily['date'], y = df_hosp_daily['admitted_covid_cum'],name='Admitted',
line = dict(color='blue', width=1)),row=1, col=3, secondary_y=False)
fig.add_trace(go.Scatter(x = df_hosp_daily['date'], y = df_hosp_daily['discharged_covid_cum'],name='Discharged',
line = dict(color='red', width=1)),row=1, col=3, secondary_y=False)
fig.add_hline(y = df_hosp_daily['admitted_covid'].mean(), line_dash="dot",line_color="blue",
annotation_text="Ave. Admitted", annotation_position="bottom left",row=1, col=3)
fig.add_hline(y = df_hosp_daily['discharged_covid'].mean(), line_dash="dot",line_color="red",
annotation_text="Ave. Discharged", annotation_position="top left",row=1, col=3)
#Graph 6
fig.add_trace(go.Scatter(x = df_pkrc_daily['date'], y = df_pkrc_daily['admitted_covid'],name='Admitted',
line = dict(color='blue', width=0.5)),row=1, col=4, secondary_y=True)
fig.add_trace(go.Scatter(x = df_pkrc_daily['date'], y = df_pkrc_daily['discharged_covid'],name='Discharged',
line = dict(color='red', width=0.5)),row=1, col=4, secondary_y=True)
fig.add_trace(go.Scatter(x = df_pkrc_daily['date'], y = df_pkrc_daily['admitted_covid_cum'],name='Admitted',
line = dict(color='blue', width=1)),row=1, col=4, secondary_y=False)
fig.add_trace(go.Scatter(x = df_pkrc_daily['date'], y = df_pkrc_daily['discharged_covid_cum'],name='Discharged',
line = dict(color='red', width=1)),row=1, col=4, secondary_y=False)
fig.add_hline(y = df_pkrc_daily['admitted_covid'].mean(), line_dash="dot",line_color="blue",
annotation_text="Ave. Admitted", annotation_position="bottom left",row=1, col=4)
fig.add_hline(y = df_pkrc_daily['discharged_covid'].mean(), line_dash="dot",line_color="red",
annotation_text="Ave. Discharged", annotation_position="top left",row=1, col=4)
#Graph 7
fig.add_trace(go.Bar(x = df_states_graph['state'], y = df_states_graph['admitted_covid'],name='Admitted Cases',
marker_color='blue'),row=2, col=3, secondary_y=False)
fig.add_trace(go.Bar(x = df_states_graph['state'], y = df_states_graph['discharged_covid'],name='Discharged Cases',
marker_color='red'),row=2, col=3, secondary_y=False)
#Graph 8
fig.add_trace(go.Bar(x = df_states_graph['state'], y = df_states_graph['vaksin_percentage_partial'],
name='Partial Vaksin (%)',marker_color='blue'),row=2, col=4, secondary_y=False)
fig.add_trace(go.Bar(x = df_states_graph['state'], y = df_states_graph['vaksin_percentage_full'],
name='Full Vaksin (%)',marker_color='red'),row=2, col=4, secondary_y=False)
#Update Fonts & Size
fig.update_annotations(font=dict(family="Helvetica", size=11))
fig.update_layout(font=dict(family="Helvetica", size=11))
fig.update_layout(height=600,showlegend=False,title_text="Malaysia Covid19 Cases Overview", title_x=0.5)
fig.update_xaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
fig.update_yaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
# add a horizontal line & annotations
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
x0=0, x1=16, xref="paper", y0=90, y1=90, yref="y",row=2, col=4)
fig.add_annotation(text='90% Vax. Pop.', x=6, y=90, arrowhead=1, showarrow=True,row=2, col=4)
#Plotting the graph
fig.show()
fig = make_subplots(rows=1, cols=4, specs=[[{'secondary_y': True}, {'secondary_y': True},
{'secondary_y': True}, {'secondary_y': True}]],
subplot_titles=('<b>Total Daily Vax.</b>',
'<b>Cumulative Daily Vax.</b>',
'<b>Full, Partial & Booster</b>',
'<b>Pfizer, Sinovac, AZ & Cansino</b>'))
#Graph 1
fig.add_trace(go.Scatter(x = df_mas_vaksin['date'], y = df_mas_vaksin['daily'],name='Total Daily Vaccinated',
line = dict(color='red',width=0.5)),row=1, col=1,secondary_y=False)
fig.add_trace(go.Scatter(x = df_mas_vaksin['date'], y = df_mas_vaksin['Vax. (%)'],name='Vax. (%)',
line = dict(color='blue',width=0.5)),row=1, col=1,secondary_y=True)
fig.add_hline(y=df_mas_vaksin['daily'].mean(), line_dash="dot",line_color="black",
annotation_text="Ave. Daily Vax.", annotation_position="top left",row=1, col=1)
#Graph 2
fig.add_trace(go.Scatter(x = df_mas_vaksin['date'], y = df_mas_vaksin['Cum. Daily'],name='Cum. Daily Vaccinated',
line = dict(color='red',width=0.5)),row=1, col=2,secondary_y=False)
fig.add_hline(y=df_mas_vaksin['Cum. Daily'].mean(), line_dash="dot",line_color="black",
annotation_text="Ave. Cum. Vax.", annotation_position="top left",row=1, col=2)
#Grpah 3
fig.add_trace(go.Scatter(x = df_mas_vaksin['date'], y = df_mas_vaksin['daily_partial'],name='Partial',
line = dict(color='red',width=0.5)),row=1, col=3,secondary_y=False)
fig.add_trace(go.Scatter(x = df_mas_vaksin['date'], y = df_mas_vaksin['daily_full'],name='Full',
line = dict(color='blue',width=0.5)),row=1, col=3,secondary_y=False)
fig.add_trace(go.Scatter(x = df_mas_vaksin['date'], y = df_mas_vaksin['daily_booster'],name='Booster',
line = dict(color='green',width=0.5)),row=1, col=3,secondary_y=True)
fig.add_hline(y=df_mas_vaksin['daily'].mean(), line_dash="dot",line_color="black",
annotation_text="Ave. Daily Vax.", annotation_position="top left",row=1, col=3)
#Graph 4
fig.add_trace(go.Scatter(x = df_mas_vaksin['date'], y = df_mas_vaksin['Total Pfizer'],name='Pfizer',
line = dict(color='red',width=0.5)),row=1, col=4,secondary_y=False)
fig.add_trace(go.Scatter(x = df_mas_vaksin['date'], y = df_mas_vaksin['Total Sinovac'],name='Sinovac',
line = dict(color='blue',width=0.5)),row=1, col=4,secondary_y=False)
fig.add_trace(go.Scatter(x = df_mas_vaksin['date'], y = df_mas_vaksin['Total AstraZ'],name='Astra Zaneca',
line = dict(color='green',width=0.5)),row=1, col=4,secondary_y=True)
fig.add_trace(go.Scatter(x = df_mas_vaksin['date'], y = df_mas_vaksin['cansino'],name='Cansino',
line = dict(color='black',width=0.5)),row=1, col=4,secondary_y=True)
fig.add_hline(y=df_mas_vaksin['daily'].mean(), line_dash="dot",line_color="black",
annotation_text="Ave. Daily Vax.", annotation_position="top left",row=1, col=4)
#Update Fonts & Size
fig.update_annotations(font=dict(family="Helvetica", size=11))
fig.update_layout(font=dict(family="Helvetica", size=11))
fig.update_layout(height=350,showlegend=False,title_text="Daily Vaccination Details", title_x=0.5)
fig.update_xaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
fig.update_yaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
#Plotting the graph
fig.show()
#Forecasting of New Cases VS Recovered Cases
#Importing the required module
#from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
#from statsmodels.tsa.statespace.varmax import VARMAX
#from statsmodels.tsa.api import VAR
#from statsmodels.tsa.stattools import grangercausalitytests, adfuller
#from tqdm import tqdm_notebook
#from itertools import product
#import statsmodels.api as sm
#import warnings
#warnings.filterwarnings('ignore')
#Importing the required datasets
#filepath_cases = 'https://raw.githubusercontent.com/MoH-Malaysia/covid19-public/main/epidemic/cases_malaysia.csv'
#filepath_deaths = "https://raw.githubusercontent.com/MoH-Malaysia/covid19-public/main/epidemic/deaths_malaysia.csv"
#filepath_vaksin = 'https://raw.githubusercontent.com/CITF-Malaysia/citf-public/main/vaccination/vax_malaysia.csv'
#Preparing the required datasets
#filepath_cases = pd.read_csv(filepath_cases, parse_dates=['date'], index_col='date').astype('float64')
#filepath_deaths = pd.read_csv(filepath_deaths, parse_dates=['date'], index_col='date').astype('float64')
#filepath_vaksin = pd.read_csv(filepath_vaksin, parse_dates=['date'], index_col='date').astype('float64')
#macro_data = pd.merge(filepath_cases,filepath_deaths,on='date')
#macro_data = pd.merge(macro_data,filepath_vaksin,on='date')
#macro_data['cases_vax_total'] = macro_data['cases_pvax'] + macro_data['cases_fvax']
#macro_data['deaths_vax_total'] = macro_data['deaths_pvax'] + macro_data['deaths_fvax']
#macro_data = macro_data[['cases_new','cases_active']]
#train_df=macro_data[:-12]
#test_df=macro_data[-360:]
#model = VAR(train_df.diff()[1:])
#sorted_order=model.select_order(maxlags=30)
#var_model = VARMAX(train_df, order=(4,0),enforce_stationarity= False)
#fitted_model = var_model.fit(disp=True)
#Creating the required forecast
#n_forecast = 180
#predict = fitted_model.get_prediction(start=len(train_df),end=len(train_df) + n_forecast-1)
#predictions=predict.predicted_mean
#predictions.columns=['cases_predicted','cases_active_predicted']
#test_vs_pred=pd.concat([test_df,predictions],axis=1)
#test_vs_pred = test_vs_pred.reset_index()
#test_vs_pred['cases_new_cum'] = test_vs_pred['cases_new'].cumsum()
#test_vs_pred['cases_active_cum'] = test_vs_pred['cases_active'].cumsum()
#test_vs_pred['cases_predicted_cum'] = test_vs_pred['cases_predicted'].cumsum()
#test_vs_pred['cases_active_predicted_cum'] = test_vs_pred['cases_active_predicted'].cumsum()
#Plotting the required forecast
#fig = make_subplots(specs=[[{'secondary_y': True}]])
#fig.add_trace(go.Scatter(x = test_vs_pred['index'], y = test_vs_pred['cases_new'], name ='Positive Cases', line = dict(color='blue', width=1)), secondary_y=True)
#fig.add_trace(go.Scatter(x = test_vs_pred['index'], y = test_vs_pred['cases_new_cum'], name ='Positive Cases', line = dict(color='blue', width=1)), secondary_y=False)
#fig.add_trace(go.Scatter(x = test_vs_pred['index'], y = test_vs_pred['cases_active'], name = 'Positive Cases After Vaccinated',line = dict(color='red', width=1)), secondary_y=True)
#fig.add_trace(go.Scatter(x = test_vs_pred['index'], y = test_vs_pred['cases_active_cum'], name = 'Positive Cases After Vaccinated',line = dict(color='red', width=1)), secondary_y=False)
#fig.add_trace(go.Scatter(x = test_vs_pred['index'], y = test_vs_pred['cases_predicted'], name = 'Predicted Positive Cases',line = dict(color='blue', width=0.75)), secondary_y=True)
#fig.add_trace(go.Scatter(x = test_vs_pred['index'], y = test_vs_pred['cases_active_predicted'], name = 'Predicted Positive Cases After Vaccinated',line = dict(color='red', width=0.75)), secondary_y=True)
#fig.update_layout(title_text='Malaysia Covid19 Forecast of Positive & Active Cases (Based on VAR Model)', title_x=0.5,showlegend=False, height=600)
#fig.update_xaxes(title_text='')
#fig.update_annotations(font=dict(family="Helvetica", size=12))
#fig.update_layout(font=dict(family="Helvetica", size=14))
#fig.update_xaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
#fig.update_yaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
#fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot", x0='2021-10-11', x1='2021-10-11', y0=0, y1=800000)
#fig.add_annotation(text='90% Adult Vax.', x='2021-10-11', y=800000, arrowhead=3, align='center', arrowsize=1, arrowwidth=2, arrowcolor="#636363",xref='x', ax=50, ay=-90, showarrow=True, xanchor="left", yanchor="bottom")
#fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot", x0='2021-11-20', x1='2021-11-20', y0=0, y1=600000)
#fig.add_annotation(text='PRN Melaka', x='2021-11-20', y=600000, arrowhead=3, align='center', arrowsize=1, arrowwidth=2, arrowcolor="#636363",xref='x', ax=50, ay=-90, showarrow=True, xanchor="left", yanchor="bottom")
#fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot", x0='2021-12-06', x1='2021-12-06', y0=0, y1=500000)
#fig.add_annotation(text='PRN Sarawak', x='2021-12-06', y=500000, arrowhead=3, align='center', arrowsize=1, arrowwidth=2, arrowcolor="#636363",xref='x', ax=50, ay=-90, showarrow=True, xanchor="left", yanchor="bottom")
#fig.show()
df = df_states_cases
df_joh = df[df.state == 'Johor']
df_ked = df[df.state == 'Kedah']
df_kel = df[df.state == 'Kelantan']
df_mel = df[df.state == 'Melaka']
df_neg = df[df.state == 'Negeri Sembilan']
df_pah = df[df.state == 'Pahang']
df_prk = df[df.state == 'Perak']
df_per = df[df.state == 'Perlis']
df_pen = df[df.state == 'Pulau Pinang']
df_sab = df[df.state == 'Sabah']
df_sar = df[df.state == 'Sarawak']
df_sel = df[df.state == 'Selangor']
df_ter = df[df.state == 'Terengganu']
df_kul = df[df.state == 'W.P. Kuala Lumpur']
df_lab = df[df.state == 'W.P. Labuan']
df_put = df[df.state == 'W.P. Putrajaya']
fig = make_subplots(rows=4, cols=4, specs=[[{'secondary_y': True}, {'secondary_y': True},
{'secondary_y': True}, {'secondary_y': True}],
[{'secondary_y': True}, {'secondary_y': True},
{'secondary_y': True}, {'secondary_y': True}],
[{'secondary_y': True}, {'secondary_y': True},
{'secondary_y': True}, {'secondary_y': True}],
[{'secondary_y': True}, {'secondary_y': True},
{'secondary_y': True}, {'secondary_y': True}]],
subplot_titles=('<b>Johor</b>', '<b>Kedah</b>', '<b>Kelantan</b>', '<b>Melaka</b>',
'<b>Negeri Sembilan</b>', '<b>Pahang</b>', '<b>Perak</b>', '<b>Perlis</b>',
'<b>Pulau Pinang</b>', '<b>Sabah</b>', '<b>Sarawak</b>', '<b>Selangor</b>',
'<b>Terengganu</b>', '<b>W.P. Kuala Lumpur</b>', '<b>W.P. Labuan</b>', '<b>W.P. Putrajaya</b>'))
#Graph 1
fig.add_trace(go.Scatter(x = df_joh['date'], y = df_joh['cases_new'],name='cases_new',
line = dict(color='red', width=0.75)), row=1, col=1,secondary_y=False)
fig.add_hline(y=df_joh['cases_new'].mean(), line_dash="dot",line_color="red",
annotation_text="Average Cases", annotation_position="top left",row=1, col=1)
fig.add_trace(go.Scatter(x = df_joh['date'], y = df_joh['cases_recovered'],name='cases_recovered',
line = dict(color='blue', width=0.75)), row=1, col=1,secondary_y=True)
#Graph 2
fig.add_trace(go.Scatter(x = df_ked['date'], y = df_ked['cases_new'],name='cases_new',
line = dict(color='red', width=0.75)), row=1, col=2,secondary_y=False)
fig.add_hline(y=df_ked['cases_new'].mean(), line_dash="dot",line_color="red",
annotation_text="Average Cases", annotation_position="top left",row=1, col=2)
fig.add_trace(go.Scatter(x = df_ked['date'], y = df_ked['cases_recovered'],name='cases_recovered',
line = dict(color='blue', width=0.75)), row=1, col=2,secondary_y=True)
#Graph 3
fig.add_trace(go.Scatter(x = df_kel['date'], y = df_kel['cases_new'],name='cases_new',
line = dict(color='red', width=0.75)), row=1, col=3,secondary_y=False)
fig.add_hline(y=df_kel['cases_new'].mean(), line_dash="dot",line_color="red",
annotation_text="Average Cases", annotation_position="top left",row=1, col=3)
fig.add_trace(go.Scatter(x = df_kel['date'], y = df_kel['cases_recovered'],name='cases_recovered',
line = dict(color='blue', width=0.75)), row=1, col=3,secondary_y=True)
#Graph 4
fig.add_trace(go.Scatter(x = df_mel['date'], y = df_mel['cases_new'],name='cases_new',
line = dict(color='red', width=0.75)), row=1, col=4,secondary_y=False)
fig.add_hline(y=df_mel['cases_new'].mean(), line_dash="dot",line_color="red",
annotation_text="Average Cases", annotation_position="top left",row=1, col=4)
fig.add_trace(go.Scatter(x = df_mel['date'], y = df_mel['cases_recovered'],name='cases_recovered',
line = dict(color='blue', width=0.75)), row=1, col=4,secondary_y=True)
#Graph 5
fig.add_trace(go.Scatter(x = df_neg['date'], y = df_neg['cases_new'],name='cases_new',
line = dict(color='red', width=0.75)), row=2, col=1,secondary_y=False)
fig.add_hline(y=df_neg['cases_new'].mean(), line_dash="dot",line_color="red",
annotation_text="Average Cases", annotation_position="top left",row=2, col=1)
fig.add_trace(go.Scatter(x = df_neg['date'], y = df_neg['cases_recovered'],name='cases_recovered',
line = dict(color='blue', width=0.75)), row=2, col=1,secondary_y=True)
#Graph 6
fig.add_trace(go.Scatter(x = df_pah['date'], y = df_pah['cases_new'],name='cases_new',
line = dict(color='red', width=0.75)), row=2, col=2,secondary_y=False)
fig.add_hline(y=df_pah['cases_new'].mean(), line_dash="dot",line_color="red",
annotation_text="Average Cases", annotation_position="top left",row=2, col=2)
fig.add_trace(go.Scatter(x = df_pah['date'], y = df_pah['cases_recovered'],name='cases_recovered',
line = dict(color='blue', width=0.75)), row=2, col=2,secondary_y=True)
#Graph 7
fig.add_trace(go.Scatter(x = df_prk['date'], y = df_prk['cases_new'],name='cases_new',
line = dict(color='red', width=0.75)), row=2, col=3,secondary_y=False)
fig.add_hline(y=df_prk['cases_new'].mean(), line_dash="dot",line_color="red",
annotation_text="Average Cases", annotation_position="top left",row=2, col=3)
fig.add_trace(go.Scatter(x = df_prk['date'], y = df_prk['cases_recovered'],name='cases_recovered',
line = dict(color='blue', width=0.75)), row=2, col=3,secondary_y=True)
#Graph 8
fig.add_trace(go.Scatter(x = df_per['date'], y = df_per['cases_new'],name='cases_new',
line = dict(color='red', width=0.75)), row=2, col=4,secondary_y=False)
fig.add_hline(y=df_per['cases_new'].mean(), line_dash="dot",line_color="red",
annotation_text="Average Cases", annotation_position="top left",row=2, col=4)
fig.add_trace(go.Scatter(x = df_per['date'], y = df_per['cases_recovered'],name='cases_recovered',
line = dict(color='blue', width=0.75)), row=2, col=4,secondary_y=True)
#Graph 9
fig.add_trace(go.Scatter(x = df_pen['date'], y = df_pen['cases_new'],name='cases_new',
line = dict(color='red', width=0.75)), row=3, col=1,secondary_y=False)
fig.add_hline(y=df_pen['cases_new'].mean(), line_dash="dot",line_color="red",
annotation_text="Average Cases", annotation_position="top left",row=3, col=1)
fig.add_trace(go.Scatter(x = df_pen['date'], y = df_pen['cases_recovered'],name='cases_recovered',
line = dict(color='blue', width=0.75)), row=3, col=1,secondary_y=True)
#Graph 10
fig.add_trace(go.Scatter(x = df_sab['date'], y = df_sab['cases_new'],name='cases_new',
line = dict(color='red', width=0.75)), row=3, col=2,secondary_y=False)
fig.add_hline(y=df_sab['cases_new'].mean(), line_dash="dot",line_color="red",
annotation_text="Average Cases", annotation_position="top left",row=3, col=2)
fig.add_trace(go.Scatter(x = df_sab['date'], y = df_sab['cases_recovered'],name='cases_recovered',
line = dict(color='blue', width=0.75)), row=3, col=2,secondary_y=True)
#Graph 11
fig.add_trace(go.Scatter(x = df_sar['date'], y = df_sar['cases_new'],name='cases_new',
line = dict(color='red', width=0.75)), row=3, col=3,secondary_y=False)
fig.add_hline(y=df_sar['cases_new'].mean(), line_dash="dot",line_color="red",
annotation_text="Average Cases", annotation_position="top left",row=3, col=3)
fig.add_trace(go.Scatter(x = df_sar['date'], y = df_sar['cases_recovered'],name='cases_recovered',
line = dict(color='blue', width=0.75)), row=3, col=3,secondary_y=True)
#Graph 12
fig.add_trace(go.Scatter(x = df_sel['date'], y = df_sel['cases_new'],name='cases_new',
line = dict(color='red', width=0.75)), row=3, col=4,secondary_y=False)
fig.add_hline(y=df_sel['cases_new'].mean(), line_dash="dot",line_color="red",
annotation_text="Average Cases", annotation_position="top left",row=3, col=4)
fig.add_trace(go.Scatter(x = df_sel['date'], y = df_sel['cases_recovered'],name='cases_recovered',
line = dict(color='blue', width=0.75)), row=3, col=4,secondary_y=True)
#Graph 13
fig.add_trace(go.Scatter(x = df_ter['date'], y = df_ter['cases_new'],name='cases_new',
line = dict(color='red', width=0.75)), row=4, col=1,secondary_y=False)
fig.add_hline(y=df_ter['cases_new'].mean(), line_dash="dot",line_color="red",
annotation_text="Average Cases", annotation_position="top left",row=4, col=1)
fig.add_trace(go.Scatter(x = df_ter['date'], y = df_ter['cases_recovered'],name='cases_recovered',
line = dict(color='blue', width=0.75)), row=4, col=1,secondary_y=True)
#Graph 14
fig.add_trace(go.Scatter(x = df_kul['date'], y = df_kul['cases_new'],name='cases_new',
line = dict(color='red', width=0.75)), row=4, col=2,secondary_y=False)
fig.add_hline(y=df_kul['cases_new'].mean(), line_dash="dot",line_color="red",
annotation_text="Average Cases", annotation_position="top left",row=4, col=2)
fig.add_trace(go.Scatter(x = df_kul['date'], y = df_kul['cases_recovered'],name='cases_recovered',
line = dict(color='blue', width=0.75)), row=4, col=2,secondary_y=True)
#Graph 15
fig.add_trace(go.Scatter(x = df_lab['date'], y = df_lab['cases_new'],name='cases_new',
line = dict(color='red', width=0.75)), row=4, col=3,secondary_y=False)
fig.add_hline(y=df_lab['cases_new'].mean(), line_dash="dot",line_color="red",
annotation_text="Average Cases", annotation_position="top left",row=4, col=3)
fig.add_trace(go.Scatter(x = df_lab['date'], y = df_lab['cases_recovered'],name='cases_recovered',
line = dict(color='blue', width=0.75)), row=4, col=3,secondary_y=True)
#Graph 16
fig.add_trace(go.Scatter(x = df_put['date'], y = df_put['cases_new'],name='cases_new',
line = dict(color='red', width=0.75)), row=4, col=4,secondary_y=False)
fig.add_hline(y=df_put['cases_new'].mean(), line_dash="dot",line_color="red",
annotation_text="Average Cases", annotation_position="top left",row=4, col=4)
fig.add_trace(go.Scatter(x = df_put['date'], y = df_put['cases_recovered'],name='cases_recovered',
line = dict(color='blue', width=0.75)), row=4, col=4,secondary_y=True)
#Adding title and adjusting the layout
fig.update_layout(height=1000,showlegend=False,title_text="Covid19 Cases & Recovered At Each States",title_x=0.5)
fig.update_annotations(font=dict(family="Helvetica", size=10))
fig.update_layout(font=dict(family="Helvetica", size=12))
fig.update_xaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
fig.update_yaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
#Plotting the graph
fig.show()
df = df_states_deaths
df['deaths_vax_total'] = df['deaths_fvax'] + df['deaths_pvax'] + df['deaths_boost']
df_joh = df[df.state == 'Johor']
df_ked = df[df.state == 'Kedah']
df_kel = df[df.state == 'Kelantan']
df_mel = df[df.state == 'Melaka']
df_neg = df[df.state == 'Negeri Sembilan']
df_pah = df[df.state == 'Pahang']
df_prk = df[df.state == 'Perak']
df_per = df[df.state == 'Perlis']
df_pen = df[df.state == 'Pulau Pinang']
df_sab = df[df.state == 'Sabah']
df_sar = df[df.state == 'Sarawak']
df_sel = df[df.state == 'Selangor']
df_ter = df[df.state == 'Terengganu']
df_kul = df[df.state == 'W.P. Kuala Lumpur']
df_lab = df[df.state == 'W.P. Labuan']
df_put = df[df.state == 'W.P. Putrajaya']
fig = make_subplots(rows=4, cols=4, specs=[[{'secondary_y': True}, {'secondary_y': True},
{'secondary_y': True}, {'secondary_y': True}],
[{'secondary_y': True}, {'secondary_y': True},
{'secondary_y': True}, {'secondary_y': True}],
[{'secondary_y': True}, {'secondary_y': True},
{'secondary_y': True}, {'secondary_y': True}],
[{'secondary_y': True}, {'secondary_y': True},
{'secondary_y': True}, {'secondary_y': True}]],
subplot_titles=('<b>Johor</b>', '<b>Kedah</b>', '<b>Kelantan</b>', '<b>Melaka</b>',
'<b>Negeri Sembilan</b>', '<b>Pahang</b>', '<b>Perak</b>', '<b>Perlis</b>',
'<b>Pulau Pinang</b>', '<b>Sabah</b>', '<b>Sarawak</b>', '<b>Selangor</b>',
'<b>Terengganu</b>', '<b>W.P. Kuala Lumpur</b>', '<b>W.P. Labuan</b>', '<b>W.P. Putrajaya</b>'))
#Graph 1
fig.add_trace(go.Scatter(x = df_joh['date'], y = df_joh['deaths_new'],name='Total Deaths',
line = dict(color='red', width=0.75)), row=1, col=1,secondary_y=False)
fig.add_hline(y=df_joh['deaths_new'].mean(), line_dash="dot",line_color="red", annotation_text="Average Deaths",
annotation_position="top left",row=1, col=1)
fig.add_trace(go.Scatter(x = df_joh['date'], y = df_joh['deaths_vax_total'],name='Total Deaths Vax',
line = dict(color='blue', width=0.75)), row=1, col=1,secondary_y=False)
#Graph 2
fig.add_trace(go.Scatter(x = df_ked['date'], y = df_ked['deaths_new'],name='Total Deaths',
line = dict(color='red', width=0.75)), row=1, col=2,secondary_y=False)
fig.add_hline(y=df_ked['deaths_new'].mean(), line_dash="dot",line_color="red", annotation_text="Average Deaths",
annotation_position="top left",row=1, col=2)
fig.add_trace(go.Scatter(x = df_ked['date'], y = df_ked['deaths_vax_total'],name='Total Deaths Vax',
line = dict(color='blue', width=0.75)), row=1, col=2,secondary_y=False)
#Graph 3
fig.add_trace(go.Scatter(x = df_kel['date'], y = df_kel['deaths_new'],name='Total Deaths',
line = dict(color='red', width=0.75)), row=1, col=3,secondary_y=False)
fig.add_hline(y=df_kel['deaths_new'].mean(), line_dash="dot",line_color="red", annotation_text="Average Deaths",
annotation_position="top left",row=1, col=3)
fig.add_trace(go.Scatter(x = df_kel['date'], y = df_kel['deaths_vax_total'],name='Total Deaths Vax',
line = dict(color='blue', width=0.75)), row=1, col=3,secondary_y=False)
#Graph 4
fig.add_trace(go.Scatter(x = df_mel['date'], y = df_mel['deaths_new'],name='Total Deaths',
line = dict(color='red', width=0.75)), row=1, col=4,secondary_y=False)
fig.add_hline(y=df_mel['deaths_new'].mean(), line_dash="dot",line_color="red", annotation_text="Average Deaths",
annotation_position="top left",row=1, col=4)
fig.add_trace(go.Scatter(x = df_mel['date'], y = df_mel['deaths_vax_total'],name='Total Deaths Vax',
line = dict(color='blue', width=0.75)), row=1, col=4,secondary_y=False)
#Graph 5
fig.add_trace(go.Scatter(x = df_neg['date'], y = df_neg['deaths_new'],name='Total Deaths',
line = dict(color='red', width=0.75)), row=2, col=1,secondary_y=False)
fig.add_hline(y=df_neg['deaths_new'].mean(), line_dash="dot",line_color="red", annotation_text="Average Deaths",
annotation_position="top left",row=2, col=1)
fig.add_trace(go.Scatter(x = df_neg['date'], y = df_neg['deaths_vax_total'],name='Total Deaths Vax',
line = dict(color='blue', width=0.75)), row=2, col=1,secondary_y=False)
#Graph 6
fig.add_trace(go.Scatter(x = df_pah['date'], y = df_pah['deaths_new'],name='Total Deaths',
line = dict(color='red', width=0.75)), row=2, col=2,secondary_y=False)
fig.add_hline(y=df_pah['deaths_new'].mean(), line_dash="dot",line_color="red", annotation_text="Average Deaths",
annotation_position="top left",row=2, col=2)
fig.add_trace(go.Scatter(x = df_pah['date'], y = df_pah['deaths_vax_total'],name='Total Deaths Vax',
line = dict(color='blue', width=0.75)), row=2, col=2,secondary_y=False)
#Graph 7
fig.add_trace(go.Scatter(x = df_prk['date'], y = df_prk['deaths_new'],name='Total Deaths',
line = dict(color='red', width=0.75)), row=2, col=3,secondary_y=False)
fig.add_hline(y=df_prk['deaths_new'].mean(), line_dash="dot",line_color="red", annotation_text="Average Deaths",
annotation_position="top left",row=2, col=3)
fig.add_trace(go.Scatter(x = df_prk['date'], y = df_prk['deaths_vax_total'],name='Total Deaths Vax',
line = dict(color='blue', width=0.75)), row=2, col=3,secondary_y=False)
#Graph 8
fig.add_trace(go.Scatter(x = df_per['date'], y = df_per['deaths_new'],name='Total Deaths',
line = dict(color='red', width=0.75)), row=2, col=4,secondary_y=False)
fig.add_hline(y=df_per['deaths_new'].mean(), line_dash="dot",line_color="red", annotation_text="Average Deaths",
annotation_position="top left",row=2, col=4)
fig.add_trace(go.Scatter(x = df_per['date'], y = df_per['deaths_vax_total'],name='Total Deaths Vax',
line = dict(color='blue', width=0.75)), row=2, col=4,secondary_y=False)
#Graph 9
fig.add_trace(go.Scatter(x = df_pen['date'], y = df_pen['deaths_new'],name='Total Deaths',
line = dict(color='red', width=0.75)), row=3, col=1,secondary_y=False)
fig.add_hline(y=df_pen['deaths_new'].mean(), line_dash="dot",line_color="red", annotation_text="Average Deaths",
annotation_position="top left",row=3, col=1)
fig.add_trace(go.Scatter(x = df_pen['date'], y = df_pen['deaths_vax_total'],name='Total Deaths Vax',
line = dict(color='blue', width=0.75)), row=3, col=1,secondary_y=False)
#Graph 10
fig.add_trace(go.Scatter(x = df_sab['date'], y = df_sab['deaths_new'],name='Total Deaths',
line = dict(color='red', width=0.75)), row=3, col=2,secondary_y=False)
fig.add_hline(y=df_sab['deaths_new'].mean(), line_dash="dot",line_color="red", annotation_text="Average Deaths",
annotation_position="top left",row=3, col=2)
fig.add_trace(go.Scatter(x = df_sab['date'], y = df_sab['deaths_vax_total'],name='Total Deaths Vax',
line = dict(color='blue', width=0.75)), row=3, col=2,secondary_y=False)
#Graph 11
fig.add_trace(go.Scatter(x = df_sar['date'], y = df_sar['deaths_new'],name='Total Deaths',
line = dict(color='red', width=0.75)), row=3, col=3,secondary_y=False)
fig.add_hline(y=df_sar['deaths_new'].mean(), line_dash="dot",line_color="red", annotation_text="Average Deaths",
annotation_position="top left",row=3, col=3)
fig.add_trace(go.Scatter(x = df_sar['date'], y = df_sar['deaths_vax_total'],name='Total Deaths Vax',
line = dict(color='blue', width=0.75)), row=3, col=3,secondary_y=False)
#Graph 12
fig.add_trace(go.Scatter(x = df_sel['date'], y = df_sel['deaths_new'],name='Total Deaths',
line = dict(color='red', width=0.75)), row=3, col=4,secondary_y=False)
fig.add_hline(y=df_sel['deaths_new'].mean(), line_dash="dot",line_color="red", annotation_text="Average Deaths",
annotation_position="top left",row=3, col=4)
fig.add_trace(go.Scatter(x = df_sel['date'], y = df_sel['deaths_vax_total'],name='Total Deaths Vax',
line = dict(color='blue', width=0.75)), row=3, col=4,secondary_y=False)
#Graph 13
fig.add_trace(go.Scatter(x = df_ter['date'], y = df_ter['deaths_new'],name='Total Deaths',
line = dict(color='red', width=0.75)), row=4, col=1,secondary_y=False)
fig.add_hline(y=df_ter['deaths_new'].mean(), line_dash="dot",line_color="red", annotation_text="Average Deaths",
annotation_position="top left",row=4, col=1)
fig.add_trace(go.Scatter(x = df_ter['date'], y = df_ter['deaths_vax_total'],name='Total Deaths Vax',
line = dict(color='blue', width=0.75)), row=4, col=1,secondary_y=False)
#Graph 14
fig.add_trace(go.Scatter(x = df_kul['date'], y = df_kul['deaths_new'],name='Total Deaths',
line = dict(color='red', width=0.75)), row=4, col=2,secondary_y=False)
fig.add_hline(y=df_kul['deaths_new'].mean(), line_dash="dot",line_color="red", annotation_text="Average Deaths",
annotation_position="top left",row=4, col=2)
fig.add_trace(go.Scatter(x = df_kul['date'], y = df_kul['deaths_vax_total'],name='Total Deaths Vax',
line = dict(color='blue', width=0.75)), row=4, col=2,secondary_y=False)
#Graph 15
fig.add_trace(go.Scatter(x = df_lab['date'], y = df_lab['deaths_new'],name='Total Deaths',
line = dict(color='red', width=0.75)), row=4, col=3,secondary_y=False)
fig.add_hline(y=df_lab['deaths_new'].mean(), line_dash="dot",line_color="red", annotation_text="Average Deaths",
annotation_position="top left",row=4, col=3)
fig.add_trace(go.Scatter(x = df_lab['date'], y = df_lab['deaths_vax_total'],name='Total Deaths Vax',
line = dict(color='blue', width=0.75)), row=4, col=3,secondary_y=False)
#Graph 16
fig.add_trace(go.Scatter(x = df_put['date'], y = df_put['deaths_new'],name='Total Deaths',
line = dict(color='red', width=0.75)), row=4, col=4,secondary_y=False)
fig.add_hline(y=df_put['deaths_new'].mean(), line_dash="dot",line_color="red", annotation_text="Average Deaths",
annotation_position="top left",row=4, col=4)
fig.add_trace(go.Scatter(x = df_put['date'], y = df_put['deaths_vax_total'],name='Total Deaths Vax',
line = dict(color='blue', width=0.75)), row=4, col=4,secondary_y=False)
#Adding title and adjusting the layout
fig.update_layout(height=1000,showlegend=False,title_text="Covid19 Daily Deaths Total & Vaccinated At Each States",title_x=0.5)
fig.update_annotations(font=dict(family="Helvetica", size=10))
fig.update_layout(font=dict(family="Helvetica", size=12))
fig.update_xaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
fig.update_yaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
#Plotting the graph
fig.show()
#Extracting And Separating Data For Each States
#Johor
df = df_states_vaksin
df_joh = df[df.state == 'Johor']
df_joh['cum_partial'] = df_joh['daily_partial'].cumsum()
df_joh['cum_full'] = df_joh['daily_full'].cumsum()
df_joh['cum_booster'] = df_joh['daily_booster'].cumsum()
#Kedah
df_ked = df[df.state == 'Kedah']
df_ked['cum_partial'] = df_ked['daily_partial'].cumsum()
df_ked['cum_full'] = df_ked['daily_full'].cumsum()
df_ked['cum_booster'] = df_ked['daily_booster'].cumsum()
#Kelantan
df_kel = df[df.state == 'Kelantan']
df_kel['cum_partial'] = df_kel['daily_partial'].cumsum()
df_kel['cum_full'] = df_kel['daily_full'].cumsum()
df_kel['cum_booster'] = df_kel['daily_booster'].cumsum()
#Melaka
df_mel = df[df.state == 'Melaka']
df_mel['cum_partial'] = df_mel['daily_partial'].cumsum()
df_mel['cum_full'] = df_mel['daily_full'].cumsum()
df_mel['cum_booster'] = df_mel['daily_booster'].cumsum()
#Negeri Sembilan
df_neg = df[df.state == 'Negeri Sembilan']
df_neg['cum_partial'] = df_neg['daily_partial'].cumsum()
df_neg['cum_full'] = df_neg['daily_full'].cumsum()
df_neg['cum_booster'] = df_neg['daily_booster'].cumsum()
#Pahang
df_pah = df[df.state == 'Pahang']
df_pah['cum_partial'] = df_pah['daily_partial'].cumsum()
df_pah['cum_full'] = df_pah['daily_full'].cumsum()
df_pah['cum_booster'] = df_pah['daily_booster'].cumsum()
#Perak
df_prk = df[df.state == 'Perak']
df_prk['cum_partial'] = df_prk['daily_partial'].cumsum()
df_prk['cum_full'] = df_prk['daily_full'].cumsum()
df_prk['cum_booster'] = df_prk['daily_booster'].cumsum()
#Perlis
df_per = df[df.state == 'Perlis']
df_per['cum_partial'] = df_per['daily_partial'].cumsum()
df_per['cum_full'] = df_per['daily_full'].cumsum()
df_per['cum_booster'] = df_per['daily_booster'].cumsum()
#Pulau Pinang
df_pen = df[df.state == 'Pulau Pinang']
df_pen['cum_partial'] = df_pen['daily_partial'].cumsum()
df_pen['cum_full'] = df_pen['daily_full'].cumsum()
df_pen['cum_booster'] = df_pen['daily_booster'].cumsum()
#Sabah
df_sab = df[df.state == 'Sabah']
df_sab['cum_partial'] = df_sab['daily_partial'].cumsum()
df_sab['cum_full'] = df_sab['daily_full'].cumsum()
df_sab['cum_booster'] = df_sab['daily_booster'].cumsum()
#Sarawak
df_sar = df[df.state == 'Sarawak']
df_sar['cum_partial'] = df_sar['daily_partial'].cumsum()
df_sar['cum_full'] = df_sar['daily_full'].cumsum()
df_sar['cum_booster'] = df_sar['daily_booster'].cumsum()
#Selangor
df_sel = df[df.state == 'Selangor']
df_sel['cum_partial'] = df_sel['daily_partial'].cumsum()
df_sel['cum_full'] = df_sel['daily_full'].cumsum()
df_sel['cum_booster'] = df_sel['daily_booster'].cumsum()
#Terengganu
df_ter = df[df.state == 'Terengganu']
df_ter['cum_partial'] = df_ter['daily_partial'].cumsum()
df_ter['cum_full'] = df_ter['daily_full'].cumsum()
df_ter['cum_booster'] = df_ter['daily_booster'].cumsum()
#Kuala Lumpur
df_kul = df[df.state == 'W.P. Kuala Lumpur']
df_kul['cum_partial'] = df_kul['daily_partial'].cumsum()
df_kul['cum_full'] = df_kul['daily_full'].cumsum()
df_kul['cum_booster'] = df_kul['daily_booster'].cumsum()
#Labuan
df_lab = df[df.state == 'W.P. Labuan']
df_lab['cum_partial'] = df_lab['daily_partial'].cumsum()
df_lab['cum_full'] = df_lab['daily_full'].cumsum()
df_lab['cum_booster'] = df_lab['daily_booster'].cumsum()
#Putrajaya
df_put = df[df.state == 'W.P. Putrajaya']
df_put['cum_partial'] = df_put['daily_partial'].cumsum()
df_put['cum_full'] = df_put['daily_full'].cumsum()
df_put['cum_booster'] = df_put['daily_booster'].cumsum()
fig = make_subplots(rows=4, cols=4, specs=[[{'secondary_y': True}, {'secondary_y': True},
{'secondary_y': True}, {'secondary_y': True}],
[{'secondary_y': True}, {'secondary_y': True},
{'secondary_y': True}, {'secondary_y': True}],
[{'secondary_y': True}, {'secondary_y': True},
{'secondary_y': True}, {'secondary_y': True}],
[{'secondary_y': True}, {'secondary_y': True},
{'secondary_y': True}, {'secondary_y': True}]],
subplot_titles=('<b>Johor</b>', '<b>Kedah</b>', '<b>Kelantan</b>', '<b>Melaka</b>',
'<b>Negeri Sembilan</b>', '<b>Pahang</b>', '<b>Perak</b>', '<b>Perlis</b>',
'<b>Pulau Pinang</b>', '<b>Sabah</b>', '<b>Sarawak</b>', '<b>Selangor</b>',
'<b>Terengganu</b>', '<b>W.P. Kuala Lumpur</b>', '<b>W.P. Labuan</b>', '<b>W.P. Putrajaya</b>'))
#Graph 1
fig.add_trace(go.Scatter(x = df_joh['date'], y = df_joh['daily_full'],name='Full',
line = dict(color='blue', width=0.75)), row=1, col=1,secondary_y=True)
fig.add_trace(go.Scatter(x = df_joh['date'], y = df_joh['daily_partial'],name='Half',
line = dict(color='red', width=0.75)), row=1, col=1,secondary_y=True)
fig.add_trace(go.Scatter(x = df_joh['date'], y = df_joh['daily_booster'],name='Booster',
line = dict(color='green', width=0.75)), row=1, col=1,secondary_y=True)
fig.add_trace(go.Scatter(x = df_joh['date'], y = df_joh['cum_full'],name='Full',
line = dict(color='blue', width=1)), row=1, col=1,secondary_y=False)
fig.add_trace(go.Scatter(x = df_joh['date'], y = df_joh['cum_partial'],name='Half',
line = dict(color='red', width=1)), row=1, col=1,secondary_y=False)
fig.add_trace(go.Scatter(x = df_joh['date'], y = df_joh['cum_booster'],name='Booster',
line = dict(color='green', width=1)), row=1, col=1,secondary_y=False)
#Graph 2
fig.add_trace(go.Scatter(x = df_ked['date'], y = df_ked['daily_full'],name='Full',
line = dict(color='blue', width=0.75)), row=1, col=2,secondary_y=True)
fig.add_trace(go.Scatter(x = df_ked['date'], y = df_ked['daily_partial'],name='Half',
line = dict(color='red', width=0.75)), row=1, col=2,secondary_y=True)
fig.add_trace(go.Scatter(x = df_ked['date'], y = df_ked['daily_booster'],name='Booster',
line = dict(color='green', width=0.75)), row=1, col=2,secondary_y=True)
fig.add_trace(go.Scatter(x = df_ked['date'], y = df_ked['cum_full'],name='Full',
line = dict(color='blue', width=1)), row=1, col=2,secondary_y=False)
fig.add_trace(go.Scatter(x = df_ked['date'], y = df_ked['cum_partial'],name='Half',
line = dict(color='red', width=1)), row=1, col=2,secondary_y=False)
fig.add_trace(go.Scatter(x = df_ked['date'], y = df_ked['cum_booster'],name='Booster',
line = dict(color='green', width=1)), row=1, col=2,secondary_y=False)
#Graph 3
fig.add_trace(go.Scatter(x = df_kel['date'], y = df_kel['daily_full'],name='Full',
line = dict(color='blue', width=0.75)), row=1, col=3,secondary_y=True)
fig.add_trace(go.Scatter(x = df_kel['date'], y = df_kel['daily_partial'],name='Half',
line = dict(color='red', width=0.75)), row=1, col=3,secondary_y=True)
fig.add_trace(go.Scatter(x = df_kel['date'], y = df_kel['daily_booster'],name='Booster',
line = dict(color='green', width=0.75)), row=1, col=3,secondary_y=True)
fig.add_trace(go.Scatter(x = df_kel['date'], y = df_kel['cum_full'],name='Full',
line = dict(color='blue', width=1)), row=1, col=3,secondary_y=False)
fig.add_trace(go.Scatter(x = df_kel['date'], y = df_kel['cum_partial'],name='Half',
line = dict(color='red', width=1)), row=1, col=3,secondary_y=False)
fig.add_trace(go.Scatter(x = df_kel['date'], y = df_kel['cum_booster'],name='Booster',
line = dict(color='green', width=1)), row=1, col=3,secondary_y=False)
#Graph 4
fig.add_trace(go.Scatter(x = df_mel['date'], y = df_mel['daily_full'],name='Full',
line = dict(color='blue', width=0.75)), row=1, col=4,secondary_y=True)
fig.add_trace(go.Scatter(x = df_mel['date'], y = df_mel['daily_partial'],name='Half',
line = dict(color='red', width=0.75)), row=1, col=4,secondary_y=True)
fig.add_trace(go.Scatter(x = df_mel['date'], y = df_mel['daily_booster'],name='Booster',
line = dict(color='green', width=0.75)), row=1, col=4,secondary_y=True)
fig.add_trace(go.Scatter(x = df_mel['date'], y = df_mel['cum_full'],name='Full',
line = dict(color='blue', width=1)), row=1, col=4,secondary_y=False)
fig.add_trace(go.Scatter(x = df_mel['date'], y = df_mel['cum_partial'],name='Half',
line = dict(color='red', width=1)), row=1, col=4,secondary_y=False)
fig.add_trace(go.Scatter(x = df_mel['date'], y = df_mel['cum_booster'],name='Booster',
line = dict(color='green', width=1)), row=1, col=4,secondary_y=False)
#Graph 5
fig.add_trace(go.Scatter(x = df_neg['date'], y = df_neg['daily_full'],name='Full',
line = dict(color='blue', width=0.75)), row=2, col=1,secondary_y=True)
fig.add_trace(go.Scatter(x = df_neg['date'], y = df_neg['daily_partial'],name='Half',
line = dict(color='red', width=0.75)), row=2, col=1,secondary_y=True)
fig.add_trace(go.Scatter(x = df_neg['date'], y = df_neg['daily_booster'],name='Booster',
line = dict(color='green', width=0.75)), row=2, col=1,secondary_y=True)
fig.add_trace(go.Scatter(x = df_neg['date'], y = df_neg['cum_full'],name='Full',
line = dict(color='blue', width=1)), row=2, col=1,secondary_y=False)
fig.add_trace(go.Scatter(x = df_neg['date'], y = df_neg['cum_partial'],name='Half',
line = dict(color='red', width=1)), row=2, col=1,secondary_y=False)
fig.add_trace(go.Scatter(x = df_neg['date'], y = df_neg['cum_booster'],name='Booster',
line = dict(color='green', width=1)), row=2, col=1,secondary_y=False)
#Graph 6
fig.add_trace(go.Scatter(x = df_pah['date'], y = df_pah['daily_full'],name='Full',
line = dict(color='blue', width=0.75)), row=2, col=2,secondary_y=True)
fig.add_trace(go.Scatter(x = df_pah['date'], y = df_pah['daily_partial'],name='Half',
line = dict(color='red', width=0.75)), row=2, col=2,secondary_y=True)
fig.add_trace(go.Scatter(x = df_pah['date'], y = df_pah['daily_booster'],name='Booster',
line = dict(color='green', width=0.75)), row=2, col=2,secondary_y=True)
fig.add_trace(go.Scatter(x = df_pah['date'], y = df_pah['cum_full'],name='Full',
line = dict(color='blue', width=1)), row=2, col=2,secondary_y=False)
fig.add_trace(go.Scatter(x = df_pah['date'], y = df_pah['cum_partial'],name='Half',
line = dict(color='red', width=1)), row=2, col=2,secondary_y=False)
fig.add_trace(go.Scatter(x = df_pah['date'], y = df_pah['cum_booster'],name='Booster',
line = dict(color='green', width=1)), row=2, col=2,secondary_y=False)
#Graph 7
fig.add_trace(go.Scatter(x = df_prk['date'], y = df_prk['daily_full'],name='Full',
line = dict(color='blue', width=0.75)), row=2, col=3,secondary_y=True)
fig.add_trace(go.Scatter(x = df_prk['date'], y = df_prk['daily_partial'],name='Half',
line = dict(color='red', width=0.75)), row=2, col=3,secondary_y=True)
fig.add_trace(go.Scatter(x = df_prk['date'], y = df_prk['daily_booster'],name='Booster',
line = dict(color='green', width=0.75)), row=2, col=3,secondary_y=True)
fig.add_trace(go.Scatter(x = df_prk['date'], y = df_prk['cum_full'],name='Full',
line = dict(color='blue', width=1)), row=2, col=3,secondary_y=False)
fig.add_trace(go.Scatter(x = df_prk['date'], y = df_prk['cum_partial'],name='Half',
line = dict(color='red', width=1)), row=2, col=3,secondary_y=False)
fig.add_trace(go.Scatter(x = df_prk['date'], y = df_prk['cum_booster'],name='Booster',
line = dict(color='green', width=1)), row=2, col=3,secondary_y=False)
#Graph 8
fig.add_trace(go.Scatter(x = df_per['date'], y = df_per['daily_full'],name='Full',
line = dict(color='blue', width=0.75)), row=2, col=4,secondary_y=True)
fig.add_trace(go.Scatter(x = df_per['date'], y = df_per['daily_partial'],name='Half',
line = dict(color='red', width=0.75)), row=2, col=4,secondary_y=True)
fig.add_trace(go.Scatter(x = df_per['date'], y = df_per['daily_booster'],name='Booster',
line = dict(color='green', width=0.75)), row=2, col=4,secondary_y=True)
fig.add_trace(go.Scatter(x = df_per['date'], y = df_per['cum_full'],name='Full',
line = dict(color='blue', width=1)), row=2, col=4,secondary_y=False)
fig.add_trace(go.Scatter(x = df_per['date'], y = df_per['cum_partial'],name='Half',
line = dict(color='red', width=1)), row=2, col=4,secondary_y=False)
fig.add_trace(go.Scatter(x = df_per['date'], y = df_per['cum_booster'],name='Booster',
line = dict(color='green', width=1)), row=2, col=4,secondary_y=False)
#Graph 9
fig.add_trace(go.Scatter(x = df_pen['date'], y = df_pen['daily_full'],name='Full',
line = dict(color='blue', width=0.75)), row=3, col=1,secondary_y=True)
fig.add_trace(go.Scatter(x = df_pen['date'], y = df_pen['daily_partial'],name='Half',
line = dict(color='red', width=0.75)), row=3, col=1,secondary_y=True)
fig.add_trace(go.Scatter(x = df_pen['date'], y = df_pen['daily_booster'],name='Booster',
line = dict(color='green', width=0.75)), row=3, col=1,secondary_y=True)
fig.add_trace(go.Scatter(x = df_pen['date'], y = df_pen['cum_full'],name='Full',
line = dict(color='blue', width=1)), row=3, col=1,secondary_y=False)
fig.add_trace(go.Scatter(x = df_pen['date'], y = df_pen['cum_partial'],name='Half',
line = dict(color='red', width=1)), row=3, col=1,secondary_y=False)
fig.add_trace(go.Scatter(x = df_pen['date'], y = df_pen['cum_booster'],name='Booster',
line = dict(color='green', width=1)), row=3, col=1,secondary_y=False)
#Graph 10
fig.add_trace(go.Scatter(x = df_sab['date'], y = df_sab['daily_full'],name='Full',
line = dict(color='blue', width=0.75)), row=3, col=2,secondary_y=True)
fig.add_trace(go.Scatter(x = df_sab['date'], y = df_sab['daily_partial'],name='Half',
line = dict(color='red', width=0.75)), row=3, col=2,secondary_y=True)
fig.add_trace(go.Scatter(x = df_sab['date'], y = df_sab['daily_booster'],name='Booster',
line = dict(color='green', width=0.75)), row=3, col=2,secondary_y=True)
fig.add_trace(go.Scatter(x = df_sab['date'], y = df_sab['cum_full'],name='Full',
line = dict(color='blue', width=1)), row=3, col=2,secondary_y=False)
fig.add_trace(go.Scatter(x = df_sab['date'], y = df_sab['cum_partial'],name='Half',
line = dict(color='red', width=1)), row=3, col=2,secondary_y=False)
fig.add_trace(go.Scatter(x = df_sab['date'], y = df_sab['cum_booster'],name='Booster',
line = dict(color='green', width=1)), row=3, col=2,secondary_y=False)
#Graph 11
fig.add_trace(go.Scatter(x = df_sar['date'], y = df_sar['daily_full'],name='Full',
line = dict(color='blue', width=0.75)), row=3, col=3,secondary_y=True)
fig.add_trace(go.Scatter(x = df_sar['date'], y = df_sar['daily_partial'],name='Half',
line = dict(color='red', width=0.75)), row=3, col=3,secondary_y=True)
fig.add_trace(go.Scatter(x = df_sar['date'], y = df_sar['daily_booster'],name='Booster',
line = dict(color='green', width=0.75)), row=3, col=3,secondary_y=True)
fig.add_trace(go.Scatter(x = df_sar['date'], y = df_sar['cum_full'],name='Full',
line = dict(color='blue', width=1)), row=3, col=3,secondary_y=False)
fig.add_trace(go.Scatter(x = df_sar['date'], y = df_sar['cum_partial'],name='Half',
line = dict(color='red', width=1)), row=3, col=3,secondary_y=False)
fig.add_trace(go.Scatter(x = df_sar['date'], y = df_sar['cum_booster'],name='Booster',
line = dict(color='green', width=1)), row=3, col=3,secondary_y=False)
#Graph 12
fig.add_trace(go.Scatter(x = df_sel['date'], y = df_sel['daily_full'],name='Full',
line = dict(color='blue', width=0.75)), row=3, col=4,secondary_y=True)
fig.add_trace(go.Scatter(x = df_sel['date'], y = df_sel['daily_partial'],name='Half',
line = dict(color='red', width=0.75)), row=3, col=4,secondary_y=True)
fig.add_trace(go.Scatter(x = df_sel['date'], y = df_sel['daily_booster'],name='Booster',
line = dict(color='green', width=0.75)), row=3, col=4,secondary_y=True)
fig.add_trace(go.Scatter(x = df_sel['date'], y = df_sel['cum_full'],name='Full',
line = dict(color='blue', width=1)), row=3, col=4,secondary_y=False)
fig.add_trace(go.Scatter(x = df_sel['date'], y = df_sel['cum_partial'],name='Half',
line = dict(color='red', width=1)), row=3, col=4,secondary_y=False)
fig.add_trace(go.Scatter(x = df_sel['date'], y = df_sel['cum_booster'],name='Booster',
line = dict(color='green', width=1)), row=3, col=4,secondary_y=False)
#Graph 13
fig.add_trace(go.Scatter(x = df_ter['date'], y = df_ter['daily_full'],name='Full',
line = dict(color='blue', width=0.75)), row=4, col=1,secondary_y=True)
fig.add_trace(go.Scatter(x = df_ter['date'], y = df_ter['daily_partial'],name='Half',
line = dict(color='red', width=0.75)), row=4, col=1,secondary_y=True)
fig.add_trace(go.Scatter(x = df_ter['date'], y = df_ter['daily_booster'],name='Booster',
line = dict(color='green', width=0.75)), row=4, col=1,secondary_y=True)
fig.add_trace(go.Scatter(x = df_ter['date'], y = df_ter['cum_full'],name='Full',
line = dict(color='blue', width=1)), row=4, col=1,secondary_y=False)
fig.add_trace(go.Scatter(x = df_ter['date'], y = df_ter['cum_partial'],name='Half',
line = dict(color='red', width=1)), row=4, col=1,secondary_y=False)
fig.add_trace(go.Scatter(x = df_ter['date'], y = df_ter['cum_booster'],name='Booster',
line = dict(color='green', width=1)), row=4, col=1,secondary_y=False)
#Graph 14
fig.add_trace(go.Scatter(x = df_kul['date'], y = df_kul['daily_full'],name='Full',
line = dict(color='blue', width=0.75)), row=4, col=2,secondary_y=True)
fig.add_trace(go.Scatter(x = df_kul['date'], y = df_kul['daily_partial'],name='Half',
line = dict(color='red', width=0.75)), row=4, col=2,secondary_y=True)
fig.add_trace(go.Scatter(x = df_kul['date'], y = df_kul['daily_booster'],name='Booster',
line = dict(color='green', width=0.75)), row=4, col=2,secondary_y=True)
fig.add_trace(go.Scatter(x = df_kul['date'], y = df_kul['cum_full'],name='Full',
line = dict(color='blue', width=1)), row=4, col=2,secondary_y=False)
fig.add_trace(go.Scatter(x = df_kul['date'], y = df_kul['cum_partial'],name='Half',
line = dict(color='red', width=1)), row=4, col=2,secondary_y=False)
fig.add_trace(go.Scatter(x = df_kul['date'], y = df_kul['cum_booster'],name='Booster',
line = dict(color='green', width=1)), row=4, col=2,secondary_y=False)
#Graph 15
fig.add_trace(go.Scatter(x = df_lab['date'], y = df_lab['daily_full'],name='Full',
line = dict(color='blue', width=0.75)), row=4, col=3,secondary_y=True)
fig.add_trace(go.Scatter(x = df_lab['date'], y = df_lab['daily_partial'],name='Half',
line = dict(color='red', width=0.75)), row=4, col=3,secondary_y=True)
fig.add_trace(go.Scatter(x = df_lab['date'], y = df_lab['daily_booster'],name='Booster',
line = dict(color='green', width=0.75)), row=4, col=3,secondary_y=True)
fig.add_trace(go.Scatter(x = df_lab['date'], y = df_lab['cum_full'],name='Full',
line = dict(color='blue', width=1)), row=4, col=3,secondary_y=False)
fig.add_trace(go.Scatter(x = df_lab['date'], y = df_lab['cum_partial'],name='Half',
line = dict(color='red', width=1)), row=4, col=3,secondary_y=False)
fig.add_trace(go.Scatter(x = df_lab['date'], y = df_lab['cum_booster'],name='Booster',
line = dict(color='green', width=1)), row=4, col=3,secondary_y=False)
#Graph 16
fig.add_trace(go.Scatter(x = df_put['date'], y = df_put['daily_full'],name='Full',
line = dict(color='blue', width=0.75)), row=4, col=4,secondary_y=True)
fig.add_trace(go.Scatter(x = df_put['date'], y = df_put['daily_partial'],name='Half',
line = dict(color='red', width=0.75)), row=4, col=4,secondary_y=True)
fig.add_trace(go.Scatter(x = df_put['date'], y = df_put['daily_booster'],name='Booster',
line = dict(color='green', width=0.75)), row=4, col=4,secondary_y=True)
fig.add_trace(go.Scatter(x = df_put['date'], y = df_put['cum_full'],name='Full',
line = dict(color='blue', width=1)), row=4, col=4,secondary_y=False)
fig.add_trace(go.Scatter(x = df_put['date'], y = df_put['cum_partial'],name='Half',
line = dict(color='red', width=1)), row=4, col=4,secondary_y=False)
fig.add_trace(go.Scatter(x = df_put['date'], y = df_put['cum_booster'],name='Booster',
line = dict(color='green', width=1)), row=4, col=4,secondary_y=False)
#Adding title and adjusting the layout
fig.update_layout(height=1000,showlegend=False,title_text="Covid19 Daily Vaccination (1D, 2D & 3D) At Each States",title_x=0.5)
fig.update_annotations(font=dict(family="Helvetica", size=10))
fig.update_layout(font=dict(family="Helvetica", size=12))
fig.update_xaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
fig.update_yaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
#Plotting the graph
fig.show()
dfpop = df_states_pop
#Creating new vax percentage columns at each states
#Johor
df_joh_pop = dfpop[dfpop.state == 'Johor']
df_joh_pop = df_joh_pop.at[df_joh_pop.index[0],'pop']
df_joh['2 Dos (%)'] = (df_joh['cum_full']/df_joh_pop)*100
#Kedah
df_ked_pop = dfpop[dfpop.state == 'Kedah']
df_ked_pop = df_ked_pop.at[df_ked_pop.index[0],'pop']
df_ked['2 Dos (%)'] = (df_ked['cum_full']/df_ked_pop)*100
#Kelantan
df_kel_pop = dfpop[dfpop.state == 'Kelantan']
df_kel_pop = df_kel_pop.at[df_kel_pop.index[0],'pop']
df_kel['2 Dos (%)'] = (df_kel['cum_full']/df_kel_pop)*100
#Melaka
df_mel_pop = dfpop[dfpop.state == 'Melaka']
df_mel_pop = df_mel_pop.at[df_mel_pop.index[0],'pop']
df_mel['2 Dos (%)'] = (df_mel['cum_full']/df_mel_pop)*100
#Negeri Sembilan
df_neg_pop = dfpop[dfpop.state == 'Negeri Sembilan']
df_neg_pop = df_neg_pop.at[df_neg_pop.index[0],'pop']
df_neg['2 Dos (%)'] = (df_neg['cum_full']/df_neg_pop)*100
#Pahang
df_pah_pop = dfpop[dfpop.state == 'Pahang']
df_pah_pop = df_pah_pop.at[df_pah_pop.index[0],'pop']
df_pah['2 Dos (%)'] = (df_pah['cum_full']/df_pah_pop)*100
#Perak
df_prk_pop = dfpop[dfpop.state == 'Perak']
df_prk_pop = df_prk_pop.at[df_prk_pop.index[0],'pop']
df_prk['2 Dos (%)'] = (df_prk['cum_full']/df_prk_pop)*100
#Perlis
df_per_pop = dfpop[dfpop.state == 'Perlis']
df_per_pop = df_per_pop.at[df_per_pop.index[0],'pop']
df_per['2 Dos (%)'] = (df_per['cum_full']/df_per_pop)*100
#Penang
df_pen_pop = dfpop[dfpop.state == 'Pulau Pinang']
df_pen_pop = df_pen_pop.at[df_pen_pop.index[0],'pop']
df_pen['2 Dos (%)'] = (df_pen['cum_full']/df_pen_pop)*100
#Sabah
df_sab_pop = dfpop[dfpop.state == 'Sabah']
df_sab_pop = df_sab_pop.at[df_sab_pop.index[0],'pop']
df_sab['2 Dos (%)'] = (df_sab['cum_full']/df_sab_pop)*100
#Sarawak
df_sar_pop = dfpop[dfpop.state == 'Sarawak']
df_sar_pop = df_sar_pop.at[df_sar_pop.index[0],'pop']
df_sar['2 Dos (%)'] = (df_sar['cum_full']/df_sar_pop)*100
#Selangor
df_sel_pop = dfpop[dfpop.state == 'Selangor']
df_sel_pop = df_sel_pop.at[df_sel_pop.index[0],'pop']
df_sel['2 Dos (%)'] = (df_sel['cum_full']/df_sel_pop)*100
#Terengganu
df_ter_pop = dfpop[dfpop.state == 'Terengganu']
df_ter_pop = df_ter_pop.at[df_ter_pop.index[0],'pop']
df_ter['2 Dos (%)'] = (df_ter['cum_full']/df_ter_pop)*100
#Kuala Lumpur
df_kul_pop = dfpop[dfpop.state == 'W.P. Kuala Lumpur']
df_kul_pop = df_kul_pop.at[df_kul_pop.index[0],'pop']
df_kul['2 Dos (%)'] = (df_kul['cum_full']/df_kul_pop)*100
#Labuan
df_lab_pop = dfpop[dfpop.state == 'W.P. Labuan']
df_lab_pop = df_lab_pop.at[df_lab_pop.index[0],'pop']
df_lab['2 Dos (%)'] = (df_lab['cum_full']/df_lab_pop)*100
#Putrajaya
df_put_pop = dfpop[dfpop.state == 'W.P. Putrajaya']
df_put_pop = df_put_pop.at[df_put_pop.index[0],'pop']
df_put['2 Dos (%)'] = (df_put['cum_full']/df_put_pop)*100
#Start creating the graph
fig = make_subplots(rows=4, cols=4, specs=[[{'secondary_y': True}, {'secondary_y': True},
{'secondary_y': True}, {'secondary_y': True}],
[{'secondary_y': True}, {'secondary_y': True},
{'secondary_y': True}, {'secondary_y': True}],
[{'secondary_y': True}, {'secondary_y': True},
{'secondary_y': True}, {'secondary_y': True}],
[{'secondary_y': True}, {'secondary_y': True},
{'secondary_y': True}, {'secondary_y': True}]],
subplot_titles=('<b>Johor</b>', '<b>Kedah</b>', '<b>Kelantan</b>', '<b>Melaka</b>',
'<b>Negeri Sembilan</b>', '<b>Pahang</b>', '<b>Perak</b>', '<b>Perlis</b>',
'<b>Pulau Pinang</b>', '<b>Sabah</b>', '<b>Sarawak</b>', '<b>Selangor</b>',
'<b>Terengganu</b>', '<b>W.P. Kuala Lumpur</b>', '<b>W.P. Labuan</b>', '<b>W.P. Putrajaya</b>'))
#Graph 1
fig.add_trace(go.Scatter(x = df_joh['date'], y = df_joh['2 Dos (%)'],name='2 Dos (%)',
line = dict(color='red', width=0.75)), row=1, col=1,secondary_y=False)
fig.add_trace(go.Scatter(x = df_joh['date'], y = df_joh['daily'],name='Total Daily Vax',
line = dict(color='blue', width=0.75)), row=1, col=1,secondary_y=True)
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
x0='2021-3-1', x1=df_date_end.at[df_date_end.index[0],'date'], xref="x", y0=90, y1=90,
yref="y", row=1, col=1)
fig.add_annotation(text='90% Vax. Pop.', x='2021-3-1', y=90, arrowhead=1, arrowsize=1, arrowwidth=1,
ax=50, ay=-10, showarrow=True, xanchor="left", yanchor="bottom", row=1, col=1)
#Graph 2
fig.add_trace(go.Scatter(x = df_ked['date'], y = df_ked['2 Dos (%)'],name='2 Dos (%)',
line = dict(color='red', width=0.75)), row=1, col=2,secondary_y=False)
fig.add_trace(go.Scatter(x = df_ked['date'], y = df_ked['daily'],name='Total Daily Vax',
line = dict(color='blue', width=0.75)), row=1, col=2,secondary_y=True)
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
x0='2021-3-1', x1=df_date_end.at[df_date_end.index[0],'date'], xref="x", y0=90, y1=90,
yref="y", row=1, col=2)
fig.add_annotation(text='90% Vax. Pop.', x='2021-3-1', y=90, arrowhead=1, arrowsize=1, arrowwidth=1,
ax=50, ay=-10, showarrow=True, xanchor="left", yanchor="bottom", row=1, col=2)
#Graph 3
fig.add_trace(go.Scatter(x = df_kel['date'], y = df_kel['2 Dos (%)'],name='2 Dos (%)',
line = dict(color='red', width=0.75)), row=1, col=3,secondary_y=False)
fig.add_trace(go.Scatter(x = df_kel['date'], y = df_kel['daily'],name='Total Daily Vax',
line = dict(color='blue', width=0.75)), row=1, col=3,secondary_y=True)
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
x0='2021-3-1', x1=df_date_end.at[df_date_end.index[0],'date'], xref="x", y0=90, y1=90,
yref="y", row=1, col=3)
fig.add_annotation(text='90% Vax. Pop.', x='2021-3-1', y=90, arrowhead=1, arrowsize=1, arrowwidth=1,
ax=50, ay=-10, showarrow=True, xanchor="left", yanchor="bottom", row=1, col=3)
#Graph 4
fig.add_trace(go.Scatter(x = df_mel['date'], y = df_mel['2 Dos (%)'],name='2 Dos (%)',
line = dict(color='red', width=0.75)), row=1, col=4,secondary_y=False)
fig.add_trace(go.Scatter(x = df_mel['date'], y = df_mel['daily'],name='Total Daily Vax',
line = dict(color='blue', width=0.75)), row=1, col=4,secondary_y=True)
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
x0='2021-3-1', x1=df_date_end.at[df_date_end.index[0],'date'], xref="x", y0=90, y1=90,
yref="y", row=1, col=4)
fig.add_annotation(text='90% Vax. Pop.', x='2021-3-1', y=90, arrowhead=1, arrowsize=1, arrowwidth=1,
ax=50, ay=-10, showarrow=True, xanchor="left", yanchor="bottom", row=1, col=4)
#Graph 5
fig.add_trace(go.Scatter(x = df_neg['date'], y = df_neg['2 Dos (%)'],name='2 Dos (%)',
line = dict(color='red', width=0.75)), row=2, col=1,secondary_y=False)
fig.add_trace(go.Scatter(x = df_neg['date'], y = df_neg['daily'],name='Total Daily Vax',
line = dict(color='blue', width=0.75)), row=2, col=1,secondary_y=True)
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
x0='2021-3-1', x1=df_date_end.at[df_date_end.index[0],'date'], xref="x", y0=90, y1=90,
yref="y", row=2, col=1)
fig.add_annotation(text='90% Vax. Pop.', x='2021-3-1', y=90, arrowhead=1, arrowsize=1, arrowwidth=1,
ax=50, ay=-10, showarrow=True, xanchor="left", yanchor="bottom", row=2, col=1)
#Graph 6
fig.add_trace(go.Scatter(x = df_pah['date'], y = df_pah['2 Dos (%)'],name='2 Dos (%)',
line = dict(color='red', width=0.75)), row=2, col=2,secondary_y=False)
fig.add_trace(go.Scatter(x = df_pah['date'], y = df_pah['daily'],name='Total Daily Vax',
line = dict(color='blue', width=0.75)), row=2, col=2,secondary_y=True)
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
x0='2021-3-1', x1=df_date_end.at[df_date_end.index[0],'date'], xref="x", y0=90, y1=90,
yref="y", row=2, col=2)
fig.add_annotation(text='90% Vax. Pop.', x='2021-3-1', y=90, arrowhead=1, arrowsize=1, arrowwidth=1,
ax=50, ay=-10, showarrow=True, xanchor="left", yanchor="bottom", row=2, col=2)
#Graph 7
fig.add_trace(go.Scatter(x = df_prk['date'], y = df_prk['2 Dos (%)'],name='2 Dos (%)',
line = dict(color='red', width=0.75)), row=2, col=3,secondary_y=False)
fig.add_trace(go.Scatter(x = df_prk['date'], y = df_prk['daily'],name='Total Daily Vax',
line = dict(color='blue', width=0.75)), row=2, col=3,secondary_y=True)
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
x0='2021-3-1', x1=df_date_end.at[df_date_end.index[0],'date'], xref="x", y0=90, y1=90,
yref="y", row=2, col=3)
fig.add_annotation(text='90% Vax. Pop.', x='2021-3-1', y=90, arrowhead=1, arrowsize=1, arrowwidth=1,
ax=50, ay=-10, showarrow=True, xanchor="left", yanchor="bottom", row=2, col=3)
#Graph 8
fig.add_trace(go.Scatter(x = df_per['date'], y = df_per['2 Dos (%)'],name='2 Dos (%)',
line = dict(color='red', width=0.75)), row=2, col=4,secondary_y=False)
fig.add_trace(go.Scatter(x = df_per['date'], y = df_per['daily'],name='Total Daily Vax',
line = dict(color='blue', width=0.75)), row=2, col=4,secondary_y=True)
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
x0='2021-3-1', x1=df_date_end.at[df_date_end.index[0],'date'], xref="x", y0=90, y1=90,
yref="y", row=2, col=4)
fig.add_annotation(text='90% Vax. Pop.', x='2021-3-1', y=90, arrowhead=1, arrowsize=1, arrowwidth=1,
ax=50, ay=-10, showarrow=True, xanchor="left", yanchor="bottom", row=2, col=4)
#Graph 9
fig.add_trace(go.Scatter(x = df_pen['date'], y = df_pen['2 Dos (%)'],name='2 Dos (%)',
line = dict(color='red', width=0.75)), row=3, col=1,secondary_y=False)
fig.add_trace(go.Scatter(x = df_pen['date'], y = df_pen['daily'],name='Total Daily Vax',
line = dict(color='blue', width=0.75)), row=3, col=1,secondary_y=True)
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
x0='2021-3-1', x1=df_date_end.at[df_date_end.index[0],'date'], xref="x", y0=90, y1=90,
yref="y", row=3, col=1)
fig.add_annotation(text='90% Vax. Pop.', x='2021-3-1', y=90, arrowhead=1, arrowsize=1, arrowwidth=1,
ax=50, ay=-10, showarrow=True, xanchor="left", yanchor="bottom", row=3, col=1)
#Graph 10
fig.add_trace(go.Scatter(x = df_sab['date'], y = df_sab['2 Dos (%)'],name='2 Dos (%)',
line = dict(color='red', width=0.75)), row=3, col=2,secondary_y=False)
fig.add_trace(go.Scatter(x = df_sab['date'], y = df_sab['daily'],name='Total Daily Vax',
line = dict(color='blue', width=0.75)), row=3, col=2,secondary_y=True)
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
x0='2021-3-1', x1=df_date_end.at[df_date_end.index[0],'date'], xref="x", y0=90, y1=90,
yref="y", row=3, col=2)
fig.add_annotation(text='90% Vax. Pop.', x='2021-3-1', y=90, arrowhead=1, arrowsize=1, arrowwidth=1,
ax=50, ay=-10, showarrow=True, xanchor="left", yanchor="bottom", row=3, col=2)
#Graph 11
fig.add_trace(go.Scatter(x = df_sar['date'], y = df_sar['2 Dos (%)'],name='2 Dos (%)',
line = dict(color='red', width=0.75)), row=3, col=3,secondary_y=False)
fig.add_trace(go.Scatter(x = df_sar['date'], y = df_sar['daily'],name='Total Daily Vax',
line = dict(color='blue', width=0.75)), row=3, col=3,secondary_y=True)
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
x0='2021-3-1', x1=df_date_end.at[df_date_end.index[0],'date'], xref="x", y0=90, y1=90,
yref="y", row=3, col=3)
fig.add_annotation(text='90% Vax. Pop.', x='2021-3-1', y=90, arrowhead=1, arrowsize=1, arrowwidth=1,
ax=50, ay=-10, showarrow=True, xanchor="left", yanchor="bottom", row=3, col=3)
#Graph 12
fig.add_trace(go.Scatter(x = df_sel['date'], y = df_sel['2 Dos (%)'],name='2 Dos (%)',
line = dict(color='red', width=0.75)), row=3, col=4,secondary_y=False)
fig.add_trace(go.Scatter(x = df_sel['date'], y = df_sel['daily'],name='Total Daily Vax',
line = dict(color='blue', width=0.75)), row=3, col=4,secondary_y=True)
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
x0='2021-3-1', x1=df_date_end.at[df_date_end.index[0],'date'], xref="x", y0=90, y1=90,
yref="y", row=3, col=4)
fig.add_annotation(text='90% Vax. Pop.', x='2021-3-1', y=90, arrowhead=1, arrowsize=1, arrowwidth=1,
ax=50, ay=-10, showarrow=True, xanchor="left", yanchor="bottom", row=3, col=4)
#Graph 13
fig.add_trace(go.Scatter(x = df_ter['date'], y = df_ter['2 Dos (%)'],name='2 Dos (%)',
line = dict(color='red', width=0.75)), row=4, col=1,secondary_y=False)
fig.add_trace(go.Scatter(x = df_ter['date'], y = df_ter['daily'],name='Total Daily Vax',
line = dict(color='blue', width=0.75)), row=4, col=1,secondary_y=True)
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
x0='2021-3-1', x1=df_date_end.at[df_date_end.index[0],'date'], xref="x", y0=90, y1=90,
yref="y", row=4, col=1)
fig.add_annotation(text='90% Vax. Pop.', x='2021-3-1', y=90, arrowhead=1, arrowsize=1, arrowwidth=1,
ax=50, ay=-10, showarrow=True, xanchor="left", yanchor="bottom", row=4, col=1)
#Graph 14
fig.add_trace(go.Scatter(x = df_kul['date'], y = df_kul['2 Dos (%)'],name='2 Dos (%)',
line = dict(color='red', width=0.75)), row=4, col=2,secondary_y=False)
fig.add_trace(go.Scatter(x = df_kul['date'], y = df_kul['daily'],name='Total Daily Vax',
line = dict(color='blue', width=0.75)), row=4, col=2,secondary_y=True)
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
x0='2021-3-1', x1=df_date_end.at[df_date_end.index[0],'date'], xref="x", y0=90, y1=90,
yref="y", row=4, col=2)
fig.add_annotation(text='90% Vax. Pop.', x='2021-3-1', y=90, arrowhead=1, arrowsize=1, arrowwidth=1,
ax=50, ay=-10, showarrow=True, xanchor="left", yanchor="bottom", row=4, col=2)
#Graph 15
fig.add_trace(go.Scatter(x = df_lab['date'], y = df_lab['2 Dos (%)'],name='2 Dos (%)',
line = dict(color='red', width=0.75)), row=4, col=3,secondary_y=False)
fig.add_trace(go.Scatter(x = df_lab['date'], y = df_lab['daily'],name='Total Daily Vax',
line = dict(color='blue', width=0.75)), row=4, col=3,secondary_y=True)
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
x0='2021-3-1', x1=df_date_end.at[df_date_end.index[0],'date'], xref="x", y0=90, y1=90,
yref="y", row=4, col=3)
fig.add_annotation(text='90% Vax. Pop.', x='2021-3-1', y=90, arrowhead=1, arrowsize=1, arrowwidth=1,
ax=50, ay=-10, showarrow=True, xanchor="left", yanchor="bottom", row=4, col=3)
#Graph 16
fig.add_trace(go.Scatter(x = df_put['date'], y = df_put['2 Dos (%)'],name='2 Dos (%)',
line = dict(color='red', width=0.75)), row=4, col=4,secondary_y=False)
fig.add_trace(go.Scatter(x = df_put['date'], y = df_put['daily'],name='Total Daily Vax',
line = dict(color='blue', width=0.75)), row=4, col=4,secondary_y=True)
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
x0='2021-3-1', x1=df_date_end.at[df_date_end.index[0],'date'], xref="x", y0=90, y1=90,
yref="y", row=4, col=4)
fig.add_annotation(text='90% Vax. Pop.', x='2021-3-1', y=90, arrowhead=1, arrowsize=1, arrowwidth=1,
ax=50, ay=-10, showarrow=True, xanchor="left", yanchor="bottom", row=4, col=4)
#Adding title and adjusting the layout
fig.update_layout(height=1000,showlegend=False,
title_text="Covid19 Vaccination Against Vaccination Per Population At Each States",title_x=0.5)
fig.update_annotations(font=dict(family="Helvetica", size=10))
fig.update_layout(font=dict(family="Helvetica", size=12))
fig.update_xaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
fig.update_yaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
#Plotting the graph
fig.show()
#Retrieving dataset for Covid19 for all countries
dfworld = pd.read_csv('https://raw.githubusercontent.com/datasets/covid-19/main/data/time-series-19-covid-combined.csv')
dfworld['daily_cases'] = dfworld['Confirmed'].diff()
dfworld['daily_recover'] = dfworld['Recovered'].diff()
dfworld['daily_deaths'] = dfworld['Deaths'].diff()
dfworld2 = pd.read_csv('https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/latest/owid-covid-latest.csv')
dfworld2 = dfworld2[dfworld2['continent'].notna()]
dfworld2 = dfworld2[['location','total_cases','total_deaths','people_vaccinated',
'population','population_density','gdp_per_capita']]
dfworld2['Cases (%)'] = (dfworld2['total_cases']/dfworld2['population'])*100
dfworld2['Deaths (%)'] = (dfworld2['total_deaths']/dfworld2['total_cases'])*100
dfworld2['Vaccination (%)'] = (dfworld2['people_vaccinated']/dfworld2['population'])*100
dfworld2.rename(columns={'location':'Country','total_cases':'Total Cases','total_deaths':'Total Deaths',
'people_vaccinated':'People Vaccinated','population':'Population',
'population_density':'Population Density','gdp_per_capita':'GDP Per Kapita'},inplace=True)
df_asean = dfworld2.loc[dfworld2['Country'].isin(['Malaysia','Singapore','Thailand','Indonesia',
'Philippines','Cambodia','Laos','Vietnam',
'Myanmar','Brunei'])]
df_asean = df_asean.sort_values('Total Cases',ascending=False)
def highlight_max(s):
'''
highlight the maximum in a Series Salmon.
'''
is_max = s == s.max()
return ['background-color: salmon' if v else '' for v in is_max]
df_asean.style.set_caption("Overview Of Covid19 Cases In ASEAN Countries").set_table_styles([{
'selector': 'caption',
'props': [
('color', 'black'),
('font-size', '26px'),
("text-align", "center"),
('text-decoration', 'underline'),
('font-family','Arial'),
('text-shadow', '2px 2px 5px grey')
]},(dict
(selector='th',props=[('text-align',
'left')]))]).format(
{'Total Cases':'{:,.0f}','Total Deaths':'{:,.0f}','People Vaccinated':'{:,.0f}','Population':'{:,.0f}',
'Population Density':'{:,.1f}','GDP Per Kapita':'{:,.1f}','Cases (%)':'{:,.1f}','Deaths (%)':'{:,.1f}',
'Vaccination (%)':'{:,.1f}'}).set_properties(subset=['Country'],**{'text-align': 'left'}).hide_index().apply(
highlight_max,subset=['Total Cases','Total Deaths','People Vaccinated','Population','Population Density',
'GDP Per Kapita','Cases (%)','Deaths (%)','Vaccination (%)'])
| Country | Total Cases | Total Deaths | People Vaccinated | Population | Population Density | GDP Per Kapita | Cases (%) | Deaths (%) | Vaccination (%) |
|---|---|---|---|---|---|---|---|---|---|
| Vietnam | 10,489,319 | 42,975 | nan | 98,168,829 | 308.1 | 6,171.9 | 10.7 | 0.4 | nan |
| Indonesia | 6,041,269 | 155,937 | 198,351,438 | 276,361,788 | 145.7 | 11,188.7 | 2.2 | 2.6 | 71.8 |
| Malaysia | 4,402,234 | 35,449 | 27,643,270 | 32,776,195 | 96.3 | 26,808.2 | 13.4 | 0.8 | 84.3 |
| Thailand | 4,084,299 | 27,263 | 55,885,495 | 69,950,844 | 135.1 | 16,277.7 | 5.8 | 0.7 | 79.9 |
| Philippines | 3,683,367 | 59,982 | nan | 111,046,910 | 351.9 | 7,599.2 | 3.3 | 1.6 | nan |
| Singapore | 1,167,498 | 1,317 | 5,010,668 | 5,453,600 | 7,915.7 | 85,535.4 | 21.4 | 0.1 | 91.9 |
| Myanmar | 612,658 | 19,434 | 28,522,949 | 54,806,014 | 81.7 | 5,591.6 | 1.1 | 3.2 | 52.0 |
| Laos | 201,460 | 727 | 5,734,044 | 7,379,358 | 29.7 | 6,397.4 | 2.7 | 0.4 | 77.7 |
| Brunei | 140,364 | 218 | 412,058 | 441,532 | 81.3 | 71,809.3 | 31.8 | 0.2 | 93.3 |
| Cambodia | 136,090 | 3,055 | 14,855,108 | 16,946,446 | 90.7 | 3,645.1 | 0.8 | 2.2 | 87.7 |
#Selecting datasets for ASEAN Countries
#Malaysia
dfworld.rename(columns={'Country/Region':'Country'},inplace=True)
df_mas = dfworld[dfworld.Country == 'Malaysia']
df_mas = df_mas.reset_index(drop=True)
df_mas = df_mas.drop([0])
#Indonesia
df_ind = dfworld[dfworld.Country == 'Indonesia']
df_ind = df_ind.reset_index(drop=True)
df_ind = df_ind.drop([0])
#Philippines
df_phi = dfworld[dfworld.Country == 'Philippines']
df_phi = df_phi.reset_index(drop=True)
df_phi = df_phi.drop([0])
#Burma
df_bur = dfworld[dfworld.Country == 'Burma']
df_bur = df_bur.reset_index(drop=True)
df_bur = df_bur.drop([0])
#Singapore
df_sin = dfworld[dfworld.Country == 'Singapore']
df_sin = df_sin.reset_index(drop=True)
df_sin = df_sin.drop([0])
#Thailand
df_tha = dfworld[dfworld.Country == 'Thailand']
df_tha = df_tha.reset_index(drop=True)
df_tha = df_tha.drop([0])
#Vietnam
df_vie = dfworld[dfworld.Country == 'Vietnam']
df_vie = df_vie.reset_index(drop=True)
df_vie = df_vie.drop([0])
#Cambodia
df_cam = dfworld[dfworld.Country == 'Cambodia']
df_cam = df_cam.reset_index(drop=True)
df_cam = df_cam.drop([0])
#Brunei
df_bru = dfworld[dfworld.Country == 'Brunei']
df_bru = df_bru.reset_index(drop=True)
df_bru = df_bru.drop([0])
#Laos
df_lao = dfworld[dfworld.Country == 'Laos']
df_lao = df_lao.reset_index(drop=True)
df_lao = df_lao.drop([0])
#Creating the ASEAN Covid19 Cases Graphs
fig = make_subplots(rows=2, cols=5, specs=[[{'secondary_y': True}, {'secondary_y': True},
{'secondary_y': True}, {'secondary_y': True}, {'secondary_y': True}],
[{'secondary_y': True}, {'secondary_y': True},
{'secondary_y': True}, {'secondary_y': True}, {'secondary_y': True}]],
subplot_titles=('<b>Malaysia</b>', '<b>Indonesia</b>', '<b>Philippines</b>', '<b>Burma</b>', '<b>Singapore</b>',
'<b>Thailand</b>', '<b>Vietnam</b>', '<b>Cambodia</b>', '<b>Brunei</b>', '<b>Laos</b>'))
#Row 1
#Graph 1
fig.add_trace(go.Scatter(x = df_mas['Date'], y = df_mas['Confirmed'],name='Total Cases',
line = dict(color='red', width=2)), row=1, col=1,secondary_y=False)
fig.add_trace(go.Scatter(x = df_mas['Date'], y = df_mas['daily_cases'],name='Daily Cases',
line = dict(color='blue', width=0.75)), row=1, col=1,secondary_y=True)
#Graph 2
fig.add_trace(go.Scatter(x = df_ind['Date'], y = df_ind['Confirmed'],name='Total Cases',
line = dict(color='red', width=2)), row=1, col=2,secondary_y=False)
fig.add_trace(go.Scatter(x = df_ind['Date'], y = df_ind['daily_cases'],name='Daily Cases',
line = dict(color='blue', width=0.75)), row=1, col=2,secondary_y=True)
#Graph 3
fig.add_trace(go.Scatter(x = df_phi['Date'], y = df_phi['Confirmed'],name='Total Cases',
line = dict(color='red', width=2)), row=1, col=3,secondary_y=False)
fig.add_trace(go.Scatter(x = df_phi['Date'], y = df_phi['daily_cases'],name='Daily Cases',
line = dict(color='blue', width=0.75)), row=1, col=3,secondary_y=True)
#Graph 4
fig.add_trace(go.Scatter(x = df_bur['Date'], y = df_bur['Confirmed'],name='Total Cases',
line = dict(color='red', width=2)), row=1, col=4,secondary_y=False)
fig.add_trace(go.Scatter(x = df_bur['Date'], y = df_bur['daily_cases'],name='Daily Cases',
line = dict(color='blue', width=0.75)), row=1, col=4,secondary_y=True)
#Graph 5
fig.add_trace(go.Scatter(x = df_sin['Date'], y = df_sin['Confirmed'],name='Total Cases',
line = dict(color='red', width=2)), row=1, col=5,secondary_y=False)
fig.add_trace(go.Scatter(x = df_sin['Date'], y = df_sin['daily_cases'],name='Daily Cases',
line = dict(color='blue', width=0.75)), row=1, col=5,secondary_y=True)
#Row 2
#Graph 6
fig.add_trace(go.Scatter(x = df_tha['Date'], y = df_tha['Confirmed'],name='Total Cases',
line = dict(color='red', width=2)), row=2, col=1,secondary_y=False)
fig.add_trace(go.Scatter(x = df_tha['Date'], y = df_tha['daily_cases'],name='Daily Cases',
line = dict(color='blue', width=0.75)), row=2, col=1,secondary_y=True)
#Graph 7
fig.add_trace(go.Scatter(x = df_vie['Date'], y = df_vie['Confirmed'],name='Total Cases',
line = dict(color='red', width=2)), row=2, col=2,secondary_y=False)
fig.add_trace(go.Scatter(x = df_vie['Date'], y = df_vie['daily_cases'],name='Daily Cases',
line = dict(color='blue', width=0.75)), row=2, col=2,secondary_y=True)
#Graph 8
fig.add_trace(go.Scatter(x = df_cam['Date'], y = df_cam['Confirmed'],name='Total Cases',
line = dict(color='red', width=2)), row=2, col=3,secondary_y=False)
fig.add_trace(go.Scatter(x = df_cam['Date'], y = df_cam['daily_cases'],name='Daily Cases',
line = dict(color='blue', width=0.75)), row=2, col=3,secondary_y=True)
#Graph 9
fig.add_trace(go.Scatter(x = df_bru['Date'], y = df_bru['Confirmed'],name='Total Cases',
line = dict(color='red', width=2)), row=2, col=4,secondary_y=False)
fig.add_trace(go.Scatter(x = df_bru['Date'], y = df_bru['daily_cases'],name='Daily Cases',
line = dict(color='blue', width=0.75)), row=2, col=4,secondary_y=True)
#Graph 10
fig.add_trace(go.Scatter(x = df_lao['Date'], y = df_lao['Confirmed'],name='Total Cases',
line = dict(color='red', width=2)), row=2, col=5,secondary_y=False)
fig.add_trace(go.Scatter(x = df_lao['Date'], y = df_lao['daily_cases'],name='Daily Cases',
line = dict(color='blue', width=0.75)), row=2, col=5,secondary_y=True)
#Adding title and adjusting the layout
fig.update_layout(height=500,showlegend=False,title_text="Covid19 Total And Daily Cases At ASEAN Countries",title_x=0.5)
fig.update_annotations(font=dict(family="Helvetica", size=10))
fig.update_layout(font=dict(family="Helvetica", size=12))
fig.update_xaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
fig.update_yaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
#Plotting the graph
fig.show()
#Creating the ASEAN Covid19 Cases Graphs
fig = make_subplots(rows=2, cols=5, specs=[[{'secondary_y': True}, {'secondary_y': True},
{'secondary_y': True}, {'secondary_y': True}, {'secondary_y': True}],
[{'secondary_y': True}, {'secondary_y': True},
{'secondary_y': True}, {'secondary_y': True}, {'secondary_y': True}]],
subplot_titles=('<b>Malaysia</b>', '<b>Indonesia</b>', '<b>Philippines</b>', '<b>Burma</b>', '<b>Singapore</b>',
'<b>Thailand</b>', '<b>Vietnam</b>', '<b>Cambodia</b>', '<b>Brunei</b>', '<b>Laos</b>'))
#Row 1
#Graph 1
fig.add_trace(go.Scatter(x = df_mas['Date'], y = df_mas['Deaths'],name='Total Deaths',
line = dict(color='blue', width=2)), row=1, col=1,secondary_y=False)
fig.add_trace(go.Scatter(x = df_mas['Date'], y = df_mas['daily_deaths'],name=' Daily Deaths',
line = dict(color='red', width=0.75)), row=1, col=1,secondary_y=True)
#Graph 2
fig.add_trace(go.Scatter(x = df_ind['Date'], y = df_ind['Deaths'],name='Total Deaths',
line = dict(color='blue', width=2)), row=1, col=2,secondary_y=False)
fig.add_trace(go.Scatter(x = df_ind['Date'], y = df_ind['daily_deaths'],name=' Daily Deaths',
line = dict(color='red', width=0.75)), row=1, col=2,secondary_y=True)
#Graph 3
fig.add_trace(go.Scatter(x = df_phi['Date'], y = df_phi['Deaths'],name='Total Deaths',
line = dict(color='blue', width=2)), row=1, col=3,secondary_y=False)
fig.add_trace(go.Scatter(x = df_phi['Date'], y = df_phi['daily_deaths'],name=' Daily Deaths',
line = dict(color='red', width=0.75)), row=1, col=3,secondary_y=True)
#Graph 4
fig.add_trace(go.Scatter(x = df_bur['Date'], y = df_bur['Deaths'],name='Total Deaths',
line = dict(color='blue', width=2)), row=1, col=4,secondary_y=False)
fig.add_trace(go.Scatter(x = df_bur['Date'], y = df_bur['daily_deaths'],name=' Daily Deaths',
line = dict(color='red', width=0.75)), row=1, col=4,secondary_y=True)
#Graph 5
fig.add_trace(go.Scatter(x = df_sin['Date'], y = df_sin['Deaths'],name='Total Deaths',
line = dict(color='blue', width=2)), row=1, col=5,secondary_y=False)
fig.add_trace(go.Scatter(x = df_sin['Date'], y = df_sin['daily_deaths'],name=' Daily Deaths',
line = dict(color='red', width=0.75)), row=1, col=5,secondary_y=True)
#Row 2
#Graph 6
fig.add_trace(go.Scatter(x = df_tha['Date'], y = df_tha['Deaths'],name='Total Deaths',
line = dict(color='blue', width=2)), row=2, col=1,secondary_y=False)
fig.add_trace(go.Scatter(x = df_tha['Date'], y = df_tha['daily_deaths'],name=' Daily Deaths',
line = dict(color='red', width=0.75)), row=2, col=1,secondary_y=True)
#Graph 7
fig.add_trace(go.Scatter(x = df_vie['Date'], y = df_vie['Deaths'],name='Total Deaths',
line = dict(color='blue', width=2)), row=2, col=2,secondary_y=False)
fig.add_trace(go.Scatter(x = df_vie['Date'], y = df_vie['daily_deaths'],name=' Daily Deaths',
line = dict(color='red', width=0.75)), row=2, col=2,secondary_y=True)
#Graph 8
fig.add_trace(go.Scatter(x = df_cam['Date'], y = df_cam['Deaths'],name='Total Deaths',
line = dict(color='blue', width=2)), row=2, col=3,secondary_y=False)
fig.add_trace(go.Scatter(x = df_cam['Date'], y = df_cam['daily_deaths'],name=' Daily Deaths',
line = dict(color='red', width=0.75)), row=2, col=3,secondary_y=True)
#Graph 9
fig.add_trace(go.Scatter(x = df_bru['Date'], y = df_bru['Deaths'],name='Total Deaths',
line = dict(color='blue', width=2)), row=2, col=4,secondary_y=False)
fig.add_trace(go.Scatter(x = df_bru['Date'], y = df_bru['daily_deaths'],name=' Daily Deaths',
line = dict(color='red', width=0.75)), row=2, col=4,secondary_y=True)
#Graph 10
fig.add_trace(go.Scatter(x = df_lao['Date'], y = df_lao['Deaths'],name='Total Deaths',
line = dict(color='blue', width=2)), row=2, col=5,secondary_y=False)
fig.add_trace(go.Scatter(x = df_lao['Date'], y = df_lao['daily_deaths'],name=' Daily Deaths',
line = dict(color='red', width=0.75)), row=2, col=5,secondary_y=True)
#Adding title and adjusting the layout
fig.update_layout(height=500,showlegend=False,title_text="Covid19 Total Deaths And Daily Cases At ASEAN Countries",title_x=0.5)
fig.update_annotations(font=dict(family="Helvetica", size=10))
fig.update_layout(font=dict(family="Helvetica", size=12))
fig.update_xaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
fig.update_yaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
#Plotting the graph
fig.show()
dfworld_highest = dfworld2.sort_values('Total Cases',ascending=False).head(10)
def highlight_max(s):
'''
highlight the maximum in a Series Salmon.
'''
is_max = s == s.max()
return ['background-color: salmon' if v else '' for v in is_max]
dfworld_highest.style.set_caption("Overview Of Top 10 Highest Covid19 Cases Worldwide").set_table_styles([{
'selector': 'caption',
'props': [
('color', 'black'),
('font-size', '26px'),
("text-align", "center"),
('text-decoration', 'underline'),
('font-family','Arial'),
('text-shadow', '2px 2px 5px grey')
]},(dict
(selector='th',props=[('text-align',
'left')]))]).format(
{'Total Cases':'{:,.0f}','Total Deaths':'{:,.0f}','People Vaccinated':'{:,.0f}','Population':'{:,.0f}',
'Population Density':'{:,.1f}','GDP Per Kapita':'{:,.1f}','Cases (%)':'{:,.1f}','Deaths (%)':'{:,.1f}',
'Vaccination (%)':'{:,.1f}'}).set_properties(subset=['Country'],**{'text-align': 'left'}).hide_index().apply(
highlight_max,subset=['Total Cases','Total Deaths','People Vaccinated','Population','Population Density',
'GDP Per Kapita','Cases (%)','Deaths (%)','Vaccination (%)'])
| Country | Total Cases | Total Deaths | People Vaccinated | Population | Population Density | GDP Per Kapita | Cases (%) | Deaths (%) | Vaccination (%) |
|---|---|---|---|---|---|---|---|---|---|
| United States | 80,732,932 | 989,331 | 256,880,347 | 332,915,074 | 35.6 | 54,225.4 | 24.3 | 1.2 | 77.2 |
| India | 43,047,594 | 522,006 | 998,395,198 | 1,393,409,033 | 450.4 | 6,426.7 | 3.1 | 1.2 | 71.7 |
| Brazil | 30,275,219 | 662,396 | 182,287,352 | 213,993,441 | 25.0 | 14,103.5 | 14.1 | 2.2 | 85.2 |
| France | 27,870,086 | 144,456 | 54,034,957 | 67,422,000 | 122.6 | 38,605.7 | 41.3 | 0.5 | 80.1 |
| Germany | 23,658,211 | 133,308 | 63,731,439 | 83,900,471 | 237.0 | 45,229.2 | 28.2 | 0.6 | 76.0 |
| United Kingdom | 21,936,272 | 172,043 | 53,041,898 | 68,207,114 | 272.9 | 39,753.2 | 32.2 | 0.8 | 77.8 |
| Russia | 17,829,009 | 366,436 | 80,337,544 | 145,912,022 | 8.8 | 24,766.0 | 12.2 | 2.1 | 55.1 |
| South Korea | 16,583,220 | 21,520 | 45,014,740 | 51,305,184 | 528.0 | 35,938.4 | 32.3 | 0.1 | 87.7 |
| Italy | 15,758,002 | 161,893 | 50,749,866 | 60,367,471 | 205.9 | 35,220.1 | 26.1 | 1.0 | 84.1 |
| Turkey | 15,003,696 | 98,610 | 57,807,483 | 85,042,736 | 104.9 | 25,129.3 | 17.6 | 0.7 | 68.0 |
dfworld3 = dfworld2.sort_values('People Vaccinated',ascending=False).head(10)
def highlight_max(s):
'''
highlight the maximum in a Series Salmon.
'''
is_max = s == s.max()
return ['background-color: salmon' if v else '' for v in is_max]
dfworld3.style.set_caption("Overview Of Top 10 Highest Covid19 People Vaccinated Worldwide").set_table_styles([{
'selector': 'caption',
'props': [
('color', 'black'),
('font-size', '26px'),
("text-align", "center"),
('text-decoration', 'underline'),
('font-family','Arial'),
('text-shadow', '2px 2px 5px grey')
]},(dict
(selector='th',props=[('text-align',
'left')]))]).format(
{'Total Cases':'{:,.0f}','Total Deaths':'{:,.0f}','People Vaccinated':'{:,.0f}','Population':'{:,.0f}',
'Population Density':'{:,.1f}','GDP Per Kapita':'{:,.1f}','Cases (%)':'{:,.1f}','Deaths (%)':'{:,.1f}',
'Vaccination (%)':'{:,.1f}'}).set_properties(subset=['Country'],**{'text-align': 'left'}).hide_index().apply(
highlight_max,subset=['Total Cases','Total Deaths','People Vaccinated','Population','Population Density',
'GDP Per Kapita','Cases (%)','Deaths (%)','Vaccination (%)'])
| Country | Total Cases | Total Deaths | People Vaccinated | Population | Population Density | GDP Per Kapita | Cases (%) | Deaths (%) | Vaccination (%) |
|---|---|---|---|---|---|---|---|---|---|
| China | 663,393 | 4,657 | 1,280,156,000 | 1,444,216,102 | 147.7 | 15,308.7 | 0.0 | 0.7 | 88.6 |
| India | 43,047,594 | 522,006 | 998,395,198 | 1,393,409,033 | 450.4 | 6,426.7 | 3.1 | 1.2 | 71.7 |
| United States | 80,732,932 | 989,331 | 256,880,347 | 332,915,074 | 35.6 | 54,225.4 | 24.3 | 1.2 | 77.2 |
| Indonesia | 6,041,269 | 155,937 | 198,351,438 | 276,361,788 | 145.7 | 11,188.7 | 2.2 | 2.6 | 71.8 |
| Brazil | 30,275,219 | 662,396 | 182,287,352 | 213,993,441 | 25.0 | 14,103.5 | 14.1 | 2.2 | 85.2 |
| Pakistan | 1,527,486 | 30,368 | 133,965,057 | 225,199,929 | 255.6 | 5,034.7 | 0.7 | 2.0 | 59.5 |
| Bangladesh | 1,952,412 | 29,126 | 128,642,674 | 166,303,494 | 1,265.0 | 3,524.0 | 1.2 | 1.5 | 77.4 |
| Japan | 7,436,434 | 29,104 | 102,978,850 | 126,050,796 | 347.8 | 39,002.2 | 5.9 | 0.4 | 81.7 |
| Mexico | 5,729,270 | 323,973 | 85,708,849 | 130,262,220 | 66.4 | 17,336.5 | 4.4 | 5.7 | 65.8 |
| Russia | 17,829,009 | 366,436 | 80,337,544 | 145,912,022 | 8.8 | 24,766.0 | 12.2 | 2.1 | 55.1 |
dfworld3 = dfworld2.sort_values('Population',ascending=False).head(10)
def highlight_max(s):
'''
highlight the maximum in a Series Salmon.
'''
is_max = s == s.max()
return ['background-color: salmon' if v else '' for v in is_max]
dfworld3.style.set_caption("Overview Of Top 10 Highest Population Countries Worldwide").set_table_styles([{
'selector': 'caption',
'props': [
('color', 'black'),
('font-size', '26px'),
("text-align", "center"),
('text-decoration', 'underline'),
('font-family','Arial'),
('text-shadow', '2px 2px 5px grey')
]},(dict
(selector='th',props=[('text-align',
'left')]))]).format(
{'Total Cases':'{:,.0f}','Total Deaths':'{:,.0f}','People Vaccinated':'{:,.0f}','Population':'{:,.0f}',
'Population Density':'{:,.1f}','GDP Per Kapita':'{:,.1f}','Cases (%)':'{:,.1f}','Deaths (%)':'{:,.1f}',
'Vaccination (%)':'{:,.1f}'}).set_properties(subset=['Country'],**{'text-align': 'left'}).hide_index().apply(
highlight_max,subset=['Total Cases','Total Deaths','People Vaccinated','Population','Population Density',
'GDP Per Kapita','Cases (%)','Deaths (%)','Vaccination (%)'])
| Country | Total Cases | Total Deaths | People Vaccinated | Population | Population Density | GDP Per Kapita | Cases (%) | Deaths (%) | Vaccination (%) |
|---|---|---|---|---|---|---|---|---|---|
| China | 663,393 | 4,657 | 1,280,156,000 | 1,444,216,102 | 147.7 | 15,308.7 | 0.0 | 0.7 | 88.6 |
| India | 43,047,594 | 522,006 | 998,395,198 | 1,393,409,033 | 450.4 | 6,426.7 | 3.1 | 1.2 | 71.7 |
| United States | 80,732,932 | 989,331 | 256,880,347 | 332,915,074 | 35.6 | 54,225.4 | 24.3 | 1.2 | 77.2 |
| Indonesia | 6,041,269 | 155,937 | 198,351,438 | 276,361,788 | 145.7 | 11,188.7 | 2.2 | 2.6 | 71.8 |
| Pakistan | 1,527,486 | 30,368 | 133,965,057 | 225,199,929 | 255.6 | 5,034.7 | 0.7 | 2.0 | 59.5 |
| Brazil | 30,275,219 | 662,396 | 182,287,352 | 213,993,441 | 25.0 | 14,103.5 | 14.1 | 2.2 | 85.2 |
| Nigeria | 255,670 | 3,143 | 22,575,395 | 211,400,704 | 209.6 | 5,338.5 | 0.1 | 1.2 | 10.7 |
| Bangladesh | 1,952,412 | 29,126 | 128,642,674 | 166,303,494 | 1,265.0 | 3,524.0 | 1.2 | 1.5 | 77.4 |
| Russia | 17,829,009 | 366,436 | 80,337,544 | 145,912,022 | 8.8 | 24,766.0 | 12.2 | 2.1 | 55.1 |
| Mexico | 5,729,270 | 323,973 | 85,708,849 | 130,262,220 | 66.4 | 17,336.5 | 4.4 | 5.7 | 65.8 |
fig = make_subplots(rows=2, cols=2, specs=[[{'secondary_y': True}, {'secondary_y': True}],
[{'secondary_y': True}, {'secondary_y': True}]],
horizontal_spacing = 0.1, vertical_spacing = 0.3,
subplot_titles=('<b>World Top 10 Covid19 Positive Cases</b>',
'<b>ASEAN Covid19 Positive Cases</b>',
'<b>World Top 10 Covid19 Death Cases</b>',
'<b>ASEAN Covid19 Death Cases</b>'))
#Graph 1
fig.append_trace(go.Bar(x = dfworld_highest['Country'], y = dfworld_highest['Total Cases'],
name='Death Cases',marker_color='red'),row=1, col=1)
#Graph 2
fig.append_trace(go.Bar(x = df_asean['Country'], y = df_asean['Total Cases'],
name='Death Vax Cases',marker_color='blue'),row=1, col=2)
#Graph 3
fig.append_trace(go.Bar(x = dfworld_highest['Country'], y = dfworld_highest['Total Deaths'],
name='Death Cases',marker_color='red'),row=2, col=1)
#Graph 4
fig.append_trace(go.Bar(x = df_asean['Country'], y = df_asean['Total Deaths'],
name='Death Vax Cases',marker_color='blue'),row=2, col=2)
#Update Fonts & Size
fig.update_annotations(font=dict(family="Helvetica", size=11))
fig.update_layout(font=dict(family="Helvetica", size=11))
fig.update_layout(height=600,showlegend=False)
#fig.update_layout(height=400,showlegend=False,title_text='Yearly and Monthly Total New And Recovered Cases', title_x=0.5)
fig.update_xaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
fig.update_yaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
#Plotting the graph
fig.show()